Motion Capture Technology in Tremor and Gait Analysis

Webinar & Transcript

Dr. Jaime Hatcher-Martin: [00:00:00] Hi everyone. Thank you for joining us again today for another Synapticure webinar. For those of you who have not met me yet. I am Jamie Martin. I am the director of movement disorders here at Synapticure. And it is my pleasure today to welcome a colleague of mine, Dr. Christine Esper to talk to us about emotion analysis.

So Dr. Esper received her medical degree from the University of Illinois College of Medicine and went on to complete her neurology residency at the Harvard Partners Neurology Program. She completed a two year movement disorders fellowship with a special concentration in deep brain stimulation, or DBS under the very famous Dr. Lin DeLong. And she is currently an associate professor at Emory University School of Medicine in Atlanta, where she has served as the director of the DBS program in the past and current, excuse me, currently is the clinical director of the Emotion Capture Lab at Emory, which is what she's going to talk about today.

She's also a senior neurology consultant for the National Neurological Conditions Surveillance System for the [00:01:00] CDC, for Parkinson's disease at the CDC. And she's actively involved with AAN, the American Academy of Neurology, including recently graduating from the prestigious Women's Leading in, Women's Leading in Neurology Leadership Program.

And she's chair of the Women's Section and memberships in the Women's Leading in Academics and Subcommittee and Health Policy Subcommittees. She's also the chair of the International Parkinson and Movement Disorder Society Telemedicine Working Group, which is where I work with her now, and she's on the editorial board of Telemedicine Reports.

And her areas of interest, clinical interest, include clinical applications of motion analysis and movement disorders, epidemiology and Parkinson's, and telemedicine and movement disorders. So it is with great pleasure that I would love to turn it over to you, Dr. Esper. Thanks for coming.

Dr. Christine Esper: Thank you very much, Dr. Hatcher Martin. Okay, let me share screen.[00:02:00] 

You guys see this? You sure can. Okay, great. So as Dr. Hatcher Martin mentioned today I will be talking about motion capture technology in tremor and gait analysis. So, for disclosures, I do get royalties for up to date and funding from the CDC, as mentioned, none of these disclosures are related to the material I'm going to speak about today.

So the objectives for this talk will be, I will start with describing the history and basic features of motion capture technology, and then I will introduce some clinical applications for tremor and gait analysis. And then at the end, I'll review future directions for the motion lab. So looking back at the history of motion capture, which is also called mocap, it is the process of recording the movement of objects or people.

So these movements are recorded [00:03:00] many times per second. This dates back to as far back as 1879 with Edward Muybridge, who pioneered the technique of repetitive photography to capture motion in his famous work, The Horse in Motion. So if you look here, this is a picture of Edward, and here if you can focus on this picture and the numbers at the bottom here, you will see how many photographs will be linked together to almost look like a video.

So moving on, in 1915, rotoscoping was evolved. And this is a technique that could produce realistic movements of an animated character by using live action film footage to paint over each frame. So here we have, you know, a live video. And then this depiction from a Disney movie. So this is when Walt Disney became more [00:04:00] involved.

This was the first length animated film which used this actual technique.

So now I'd like to show you a clip of 3D motion capture, which was used in the movie Planet of the Apes. It's actually a tool that's used in media and in movies these days.

So in this clip, you can see all the dots around the patient's face, and that was used to help create. [00:05:00] pictures of the apes in this movie. So motion capture has a number of other applications. It's often used in sports medicine. You may have seen some references to this. The types of studies involved have quite a bit of variability depending on What sport is being tested, whether it's indoor or outdoor depending on the sports, the weight and size of the sensors will, will change and the capture volume in terms of what the recording can change as well as specifications of the calibration.

We do have a lab here at Emory and the orthopedic center and the Shepherd rehab center also visit us recently as they plan on building a lab as well. Motion capture is also often used in engineering. This is a systematic review of some applications for industrial engineering. And [00:06:00] this is just some examples of what was presented in this review.

It screened about 1700 articles, ultimately 60 studies were selected for this analysis. And as you can see, motion capture has grown significantly over the years. This particular study, some things that they looked at were workers and health safety improvement of industrial process of products the workers environment, for example, fatigue and posture.

They actually tested what the most appropriate posture was in certain factories and routes in which patients would navigate in factories and different lifting heights. To to lift items in a factory setting to improve efficiency and minimize any types of injuries. So next we'll shift to our focus at the 3D motion capture lab in the brain health center.[00:07:00] 

This is at Emory, the movement disorder center, and in our lab we have 14 high speed infrared sensitive digital video cameras. We also have 60 12 millimeter spherical. IR reflecting markers that we apply to standard bony landmarks on the patient in preparation for the procedure and real time marker identification and spatial tracking.

So what does this mean? So we can record this about 120 times per second with a resolution of less than 0. 7 millimeters error per marker. So here's a picture of some of the markers that we put on the body. And here is a figure that shows our lab. The recording area measures 3 meters by 4. 6 meters patient is often sitting in a stool for parts of the procedure.

And here are some of the cameras around here and here's an actual [00:08:00] photograph of the lab. Similar to what's described up here. And as you can see, all of these black lines here are the cameras surrounding the recording area.

So here's a picture of someone with the skeletal marker set on. And as you can see patients Do you need to wear a tight fitting outfit, which we provide to patients. And the reason for this is we don't like having a lot of loose garments since we're putting the markers on specific bony landmarks, we actually add a total of 60 markers on the patient.

And we have. We have several additional markers on the hands as compared to some of the other protocols that orthopedics and rehab may use. And the reason for this is because we also are looking at tremor, and it's very important to have these markers here. Essentially, once this is done, a 3D skeletal model will be created, and we obtain data for each [00:09:00] segment on the three axes, X, Y, and Z.

And this is just a clip of some of the data that we... Obtain and you can see for each area, you can see we, we obtain all of this data. And this is just a small sample of the information that we receive as part of our protocol.

So, here we have a 360 degree view of a patient sitting in the motion lab and you'll notice all the cameras surrounding this patient

and as part of our testing protocol. protocol. We have a specific set of tasks that we have the patients go through particularly because we are interested in tremor and gait. So we have both seated and standing poses with the arms at rest. We have seating and standing pose of the arms extended out and also with the arms.

in this position. And then we [00:10:00] also test for action tremors. So we do tasks such as finger, nose, finger testing and drawing a circle. And then we also have patients walk across the recording area. And we also capture turns.

So this slide show shows the 3D marker coordinates from the converging camera rays. So here we see that it's focused on the left hand during some of the action components and then the right hand as well.

And here is a video of a patient walking in the lab, just to show you a demonstration of what we're recording.

So this is as I mentioned, a pretty involved process. We get a great deal of data. After the patient goes through the [00:11:00] procedure. So once we have finished evaluating a patient motion lab, we do need to process that data. So it starts with using some properties proprietary software, where we do 3d reconstructions of the marker positions of the x, y, and z axis of each marker.

We smooth out the data if anything has been missing. Here are some pictures of what I'm referring to. Eventually, it develops a skeletal model and joint angles. And then the the data is exported to some type of code. And in our particular lab, we have a MATLAB code that processes all of that data.

And eventually we have output, which is the information that we provide as part of the clinical report. So I'm going to start with talking about some of the clinical applications of the motion capture lab, specifically with regard to tremor. So tremor is [00:12:00] an involuntary rhythmic oscillatory movement of a body part, and movement disorders society has defined this.

They started in 1998 and there was a revision in 2018. And some things to keep in mind is no tremor is perfectly rhythmic, can vary among tremors, and there's no accepted minimum rhythmicity for tremor. So this is a a clip of The a Parkinson's rating scale that we commonly use in movement disorders.

There are many things. Included in this rating scale. This is just a segment of what, how we calculate a breast tremor. And as you can see despite this detailed criteria, which is available right here, it's an ordinal scale of 0 through 4. There still is some subjectivity with this. For example, if a patient is near 1 centimeter, [00:13:00] you know, patient can be rated as a 1 or a 2, or perhaps the tremor can be near 3 centimeters, could be a 2 or a 3.

So some subjectivity and also some intra rater variability with this scale.

So there have been a number of publications that have documented quantifying tremor. Here are some examples. Some techniques do use 3D motion capture, others use wearables, which is another Hot field these days and accelerometry. So one of the purposes of our lab is to help identify and measure tremor.

So this I'll simplify this in terms of our data analysis. So to quantify tremor. We what we do is what it's called is a spectral analysis. And this has been well documented in the literature to use to quantify tremor. And if you see a clearly identifiable peak, as you [00:14:00] can see here this will suggest that a tremor is present.

There are some things done in the background in terms of the raw data. You do need to filter it at a certain frequency range in order to eliminate artifact and noise. So here's a skeletal model of a Parkinson's patient. With a right hand and right leg rest tremor. So you can see over here,

and as part of the output. We also get a visual of what we call a time series, which gives a sample of the tremor findings, the frequency over a certain period of time. So here we have the capture over 30 seconds. You know, each one of these is a second, and this is a patient with Parkinson's disease who had a right hand rust tremor.

This specifically is looking at the distal [00:15:00] marker in the thumb, and it's about a four hertz tremor.

We have a number of engineers in our lab. This is an app that was created within MATLAB by one of our Georgia Tech consultants, and it really has helped streamline the workflow for our lab. So as part of the output that we receive there are several tables similar to what I am showing here, which will specify tremor findings per anatomical region.

So here we have that here, what the task is, this patient was sitting at rest, and then specifically what the findings are. So in this case, patient had a right hand rest tremor, about 4. 14 hertz severe right hand tremor. And then what's nice is we also have at the end of the report, summary findings. Per anatomical region and tremor subtypes.

So for this [00:16:00] patient in the right hand this patient had an action dependent tremor, posture dependent tremor, as well as a resting tremor and all motor tasks. It gives you the average frequency for each each tremor subtype. So, in addition to Parkinson's tremor. We, our lab does receive some referrals to analyze other tremors.

We certainly look at essential tremor we often see patients as part of the deep brain stimulation pipeline. So we will analyze tremor before and after intervention but sometimes Patients are sent to our lab. If they have more unusual tremors, either for clarification or to get more information on that tremor.

So an example of such an unusual tremor is a Holmes tremor. This is a tremor that's present at rest exacerbated with posture meaning when the hands are put up, and often further intensified with [00:17:00] action. And this is often an irregular and coarse tremor. It's about two to five hertz, makes it a little bit more unique as compared to some other tremor types, often due to a lesion in the midbrain, and that's more in the central area of the brain, it can be due to a variety of reasons.

Some examples are multiple sclerosis, tumors, strokes, and commonly presents about one to 24 months after that insult. And depending on where the insult is, patients can certainly have other neurological findings on exam. So here's a video of this type of tremor. Okay,[00:18:00] 

so with your good hand, touch my hand and then touch her nose.

With that hand, try to touch her chin, not her nose. Cover

your eyes if you want. and then reach back towards your face.

So as you can see quite a debilitating tremor. My left hand. So we actually presented this information in last year's, well, 2022 American Academy of Neurology. And our presentation actually was selected as a platform presentation, we had a resident presenting information to look at quantitative motion analysis of clinical characteristics of [00:19:00] Holmes tremor as compared to other tremor types.

So what we looked at is our population of Holmes tremor as captured in the lab from 2014 to 2020. As I mentioned, rare tremor, only 11 cases. But we did have the Parkinson's disease patients tremor information, 289 and essential tremor, 135 comparisons. And here is the breakdown of the patients in terms of age and gender.

And as you can see, we, we match them, age and sex match them to the central tremor Parkinson's disease patients. So here's a depiction of how we analyze the data. And all the different markers on the hand. And as part of our tremor analysis workflow, we also do what we call a waterfall plot, which is described here.

And we use time varying power [00:20:00] spectrum of single markers. We have the frequencies on the access here and the relative power. Over here, and you see that these windows are overlapping every 2 to 3 seconds. And the purpose of that is we're trying to capture. Overlapping recordings because tremor can be intermittent.

So to help address that issue and try to be as accurate as possible. And as you can see here, there is a pronounced pronounced tremor at about 5. 5 hertz, which becomes apparent about 2. 5 seconds. And so I have around here. And this particular example is a example of a Parkinson's disease patient. So, as you can see, Holmes tremor was larger and more variable in amplitude, as you can see here, compared to Parkinson's disease and essential tremor was also slower in frequency [00:21:00] as compared to the other two as we expected.

And again the majority of the patients seen in our lab do have Parkinson's disease and essential tremor, but that data that we gather from those groups can help analyze other more unusual tremor subtypes as I'm describing here. So next I'm going to move on to some clinical applications of gate analysis in the 3D motion capture lab.

So here are I've outlined some, some of the data we can obtain through the 3D motion capture lab, so we, we can get all of this type of information, how fast a patient's walking how long their steps are versus their stride, which is the right and left leg moving, how many steps per minute, how, how wide this their, their feet are, [00:22:00] When they walk and then also how their legs swings.

So this is a great deal of data. I know a number of centers do have gate maps. It does provide some of this data. I don't think it provides all of it. But in addition to that, what's unique about 3D motion capture is we also get Joint angle kinematics and this is something that is extremely unique to 3d motion capture and the reason being is we have those markers all over the body.

And then, as you measure those markers in space, you can calculate the angles of different anatomical parts. So essentially we can. Document some things of interest for Parkinson's disease will be like a stooped posture. So we'll look at trunk and the pelvis, tilt arm swing. Oftentimes a patient will have as much of an arm swing.

So we do have that recorded as well as leg swing.

So here is an example of what [00:23:00] a patient will receive in a report from this type of analysis. And you'll see, as I mentioned, the spatial temporal indices for the patient as compared to normals. And then where they stand, so as you can see, this patient was quite slow walking at about 38% of a normal value.

So, so quite slow. Step and stride length also significantly diminished at about 53%. And then we have this nice figure here that's essentially this information, but it's depicted in this nice figure to help the patients and clinicians just look quickly and see. Thank you. You know, you can see here's normal at 100% and you're like, wow, look at how, how low, how much lower forward velocity step and stride length work, for example.

So, moving on to Parkinson's disease. This is one of the main focuses of our lab and [00:24:00] epidemiological studies have shown that Parkinson's disease is one of the fastest growing neurological disorders globally. You can see this. So data supported here from 1990 to 2016, so there's been a lot of interest in Parkinson's disease and studies on Parkinson's disease.

Another great area of Parkinson's disease is gait. Here's a nice depiction of progression of gait in Parkinson's disease. Here you have a woman exercising, a little bit of inversion of the right foot, mild motor impairment. As it advances, now you have a patient who has more asymmetry. As you can see, and then eventually more advanced Parkinson's disease patient has the stooped posture now is relying on a walker to get around.

So among the primary motor features of Parkinson's disease postural instability, which is a [00:25:00] balance problem in which patients often can fall backwards is the main direction but they can fall forward as well and have difficulty maintaining their balance. This. is a complaint that's often difficult to control with medications and is least responsive to the dopaminergic medications.

And as a disease advances, postural instability and gait difficulty really are major contributors to disability in patients with Parkinson's disease. So here is a picture of a patient with Parkinson's disease walking in our lab. You can see the stooped posture. Take several steps to turn reduced arm swing bilaterally.

I think the left is, is worse.

And here is some [00:26:00] information that we also get as part of a report regarding the 3D joint angles that I mentioned. This specific example is a patient we're looking at arm swing. Here's arm joint angle. And if you look at the top row in gray is what is normal. So that's what you would expect for a normal arm swing.

And. In red, this is the right arm, and in blue, it's the left arm. So both of them are markedly reduced, left a bit more than the right, but in both cases, this patient has a very reduced arm swing bilaterally. And we can actually quantify this by degrees in our lab. There are some studies that look at this, arm swing as well as leg swing.

With and without medications and then pre and post intervention, for example, this study here with deep brain stimulation therapy. So there's literature [00:27:00] that has reviewed gait kinematics and Parkinson's disease, and a number have also focused on deep brain stimulation, as I've mentioned. And a number of these do include some of the indices that I mentioned that we can record in our lab.

So, again, this is, a overview of a number of studies that have been done, and as I mentioned, a number of these spatial temporal parameters you can get from centers that do have the gate maps, but some of these specific kinematic parameters are really unique to 3D motion capture, and you can see with deep brain simulation, particularly with this target, there have been some improvements, speed, stride length Some of the, the stooped posture that you can see here.

So it's really, you can get a lot of information from this type of analysis. So one of the questions is, can we use 3D gate [00:28:00] analysis to detect early Parkinson's disease? This study looked at 44 patients with early Parkinson's disease, like quite early, about 5. 2 months. into the disease plus or minus three months and compare the data to 44 age and sex matched healthy controls.

As you can see, a number of the parameters studied, both the spatial parameters as well as the temporal parameters, were statistically significant when you compare the early Parkinson's disease patients to the healthy controls. So the question is, can this be done on a larger scale to help predict early Parkinson's disease?

There have been some studies on multiple sclerosis and use of 3D motion capture where preclinical disease a small pilot study, but preclinical disease was identified before the patients became more symptomatic. So freezing of gates [00:29:00] is another phenomenon which is a real disability for patients that experience this.

And essentially it's when a patient has brief arrests and stepping and walking, and it can be quite unpredictable. So because of this unpredictable nature, You know, it can catch a patient off guard, it can result in falls and it does not always respond to levodopa therapy. This is one of the more challenging things to control.

So I'll show you a video here of one of our skeletal models of a patient with freezing of gait. And if you focus here on the Z row, you will see the areas where it's quite flat. And this is where the patient is freezing.

Here's an example right now of the patient freezing. There's this hesitation. It's like the, the feet are glued to the floor.[00:30:00] 

The patient's able to walk again. And you'll see another episode around here.

Now, freezing often gets worse in tight spaces or when a patient is turning.

Some research in our lab has also looked at the spectral analysis, particularly of the heel markers on, on the patient's feet to be able to detect freezing of gaits. And if you look here, I'll just go through this slide with you. So here in the blue is a marker that's at the top of the head. And if I walk you through this, on the left you can see in [00:31:00] the y axis in our lab, you see the patient is walking over, I think I hear an echo, my head, and then over time here, here's the patient walking.

So if you follow this pattern, where the red arrow is. The patient's walking and is actually looking down at that moment. And then if you look here, what I've outlined, these are the heel markers. Purple's the right heel and the green is the left heel. And there's a freezing episode. Lasts about 10 seconds.

Correlates with the head looking down. And again, this is a a more focused spectral analysis of the heel markers. Here's the right heel versus the left heel. And you can see that potentially the question is. Can you predict these freezing episodes while or before they are going to happen, which may reduce and [00:32:00] less complications such as falls.

So we actually presented this at the American Academy of Neurology just a few months ago in Boston. That was this. This abstract was presented similar to the data that I showed you. We also also presented some data using analysis of patients with Parkinsonism in the motion capture lab, and identifying different motor types there's a tremor dominant motor type and a postural stability disorder subtype and machine learning.

Using that data helps identify those motor subtypes, and we also looked at the effects of thalamic deep brain stimulation in our central tremor cohort. This study has been published. It was published, I believe, in January or February in Sensors, and this one has just been submitted for [00:33:00] publication.

So here is another Area that we've looked at again, looking at gates, but we wanted to compare the effects of deep brain stimulation on gate to the max and Parkinson's disease versus a central tremor. This was a small study presented at the movement disorders society meeting last year in Madrid.

So we had 10 patients, five patients with Parkinson's disease and five of the central tremor. We looked at the data that we gathered in the lab with gate. Before DBS and again after DBS and just a quick summary of our findings step length and speed increase with Parkinson's disease, but decrease in a central tremor.

So, what that means is when they took the steps that actually. Decrease in Parkinson's disease and cadence, which is steps per minute decreased in both groups and in general DBS improve pace related gate [00:34:00] parameters in patients with Parkinson's disease. We are continuing gathering this data. This actually was from about a year ago so we have more data to present, and that we're gathering.

So next steps, you know what is normal gates we have all this information. But really, what are we comparing it to? And even so... Within the disease, what is considered normal? As you may know, with Parkinson's disease, things can fluctuate quite a bit as the disease advances. And then are they on meds, off meds?

Have they had any type of intervention? If they had surgical intervention, Is the stimulator on? Is it off? There, there are a lot of variables to look at. So there have been some studies on normal gaits, really not, not a whole lot out there. But here are some examples. Again, I think gender differences are also very interesting that we're looking at in our lab.[00:35:00] 

Here's an example of what I envision in the future. For our lab, we're trying to establish gate norms from our database. So currently we have I think about 1100 procedures that we've covered and some of them are patients before and after an intervention. So not necessarily 1100 unique patients. But what I'd like to look at is to create norms.

By age, by gender, and then by disease. So here's an example of what I'm referring to. So this was done when we had about 900 total procedures. But I'd like to look at a young onset Parkinson's patient. So here's a 48 year old female with Parkinson's disease. So we can look at her information in yellow as compared to her age range, her gender, and then overall Parkinson's patients.

So this is [00:36:00] all Parkinson's patients. In our group of 900. So as you can see this patient, we can start with forward velocity, which is like how fast the patient's walking. So for this patient, compared to the whole population of Parkinson's patients, 900, she's walking quite fast, which is what you would expect.

I mean, she is a young onset Parkinson's disease patient. But if you compare it to her gender, Okay. Still fast and again, but with her age, because you would expect younger patients with Parkinson's disease to walk similarly as quickly as compared to let's say an 80 year old patient with Parkinson's disease.

Again. It's above average, but not nearly as evident as when you compare it to the whole 900 patients. And again, you can compare some other parameters. Essentially, anything that we can collect can be compared in this [00:37:00] fashion. So here, this patient has a much longer step length as compared to the whole group of 900 Parkinson's patients.

But when you compare by age, or by gender, she's even faster. And then when you compare by age, yes, she's faster, but the, the difference is not as profound as you would see with a general population of 900 patients. So lastly future directions for the lab. So we have recently upgraded our equipment.

We are still working on the workflow, but now we have we've upgraded our cameras and our software. So it has a higher level of detail and resolution to help capture the movements that I mentioned. We have upgraded the floors in our lab. We now have an elevated floor [00:38:00] with force plates in the center of the lab.

And what that means is the force plates give you the ability to measure, measure center pressure or balance for patients. And this is we're very excited about this because balance is a. A significant concern with Parkinson's disease, and we really don't have a great way to measure it. I mean, we do do this test in the clinic.

But with our procedure, we can actually quantify the sway that a patient has before an intervention as baseline and then after intervention and to see if there's any worsening or improvement. So we are gathering that data currently. We also, this will allow us to compare even targets for deep brain stimulation.

Is one target better than another for balance disorders. And then even for essential tremor patients, sometimes we are concerned about implanting both sides [00:39:00] of the brain as that may worsen balance. So this would be a nice test where we can actually objectively quantify this. We are refined, refining our tremor analysis further to validate our findings with the clinical rating scales that I described again to help, better assess the patients clinically and also in terms of intervention with the DBS targets and look at quantitative outcomes. We're looking to establish the gate norms, as I described for all patients and then breaking it down by disease. And then the main groups that will be looking at Parkinson's disease and essential tremor but again.

We can move on further to break it down by gender, age, and so on. And then machine learning. So I did show you some examples at the start of how we've implemented some machine learning in the lab. That was the freezing of gates [00:40:00] data that I showed you where the head when they look down may predict a freezing episode.

And then the abstract that we presented at the AAN. Which can help predict motor subtypes of Parkinson's disease. So this is just the beginning of what we can do with the data that we're capturing. I'm also working with another company in which we are they're looking to design a computerized stylus on a tablet to help measure.

Deviations in tremor as part of our procedure and to see if this can help give us more information on disease, severity and progression. And then we're also looking to add sensors to our study in specific anatomical regions and to compare the data from the wearable sensors and what we're obtaining in the motion lab.

So I'd say I wouldn't be able to do [00:41:00] this by myself. I know Dr. Hatchimore knows a number of these people, but I'm I'm very thankful for my team. We have grown. Essentially the lab was started by Dr. Gary Alexander, who retired. I then took over. And it really was myself and Doug Bernard, the tech who you.

We're on our own for quite a bit of time. Dr. Factor has been very supportive, and he's very interested in freezing of gates, so has utilized Emotion Lab for a number of his studies, and since then, this is I would say in, Late 2018 early 2019. We have grown. We now have a number of engineers. We have students who joined us fellows.

It really has been a wonderful and collaborative process to help move the science forward to continue to analyze tremor and gates for Parkinson's disease. And I'm happy to answer any [00:42:00] questions. I don't know, Dr. Hatcher mind you want me to stop sharing. Sure.

Dr. Jaime Hatcher-Martin: So let's see. There's quite a few questions. Some of these you've actually already addressed. So I'll try to kind of condense these a little bit. I guess we could start with maybe tell us a little bit about patients are interested, like what can, what can they do? Are there, there are observational studies for this?

Are there, are there other motion capture labs and other studies that maybe they could get involved in to help advance the science for this? 

Dr. Christine Esper: So just so I clarify, is this a national talk or is this just people specific and 

Dr. Jaime Hatcher-Martin: it's all over. 

Dr. Christine Esper: Yeah, patients can be all over. So I will say, there aren't a whole lot of 3d motion capture labs specific for movement disorders now, although it is growing, like I know there's some on the west coast.

I'm actually part of the game motion clinical. Analysis [00:43:00] society. They are mainly orthopedics and rehab folks that that is a main focus, but Depending on your geographical location. I think we can assist with that. It's still, I would say in the earlier stages there definitely are more motion labs in Europe.

That are looking at this to a greater degree. 

Dr. Jaime Hatcher-Martin: And I always direct people to, you know, we can always check things like clinical trials. gov. We can check things like Michael J. Fox's foundations Fox trial finder to look at, you know, potential trials that are near you. So we could certainly look at those things.

So another question is, you know, you talk a lot about being able to predict freezing and things like that. If you go further earlier on in the disease, are there, has there been any studies looking at. You know, signs that would predict future freezing signs that would predict right that which groups of people are more likely to fall.

[00:44:00] So you can maybe intervene on that group ahead of time before the actual falls of the freezing starts to happen. Sure. 

Dr. Christine Esper: So that's part of our second publication that we literally just submitted last night. So that's subtypes and in general that study just looked at two subtypes. There's, there's some controversy in how many subtypes you can have.

But we looked at the tremor dominant versus the post transmissibility gait disorder subtype. And tremor dominant in general you can usually identify them pretty early on. I think Dr. Hatcher Martin would agree with me. And I deal with a fairly biased population. I, I just see the people coming through the DBS pipeline.

But you can... I would say moving to source specialists can usually identify at least between those two subgroups fairly early on in terms of the ones that tend to have more difficulties are the PIGD subtype. [00:45:00] In terms of predicting them in advance, I would say that's difficult. I mean, there are some signs and you will see that hopefully if our publication is accepted.

But the tricky thing is. You know, some patients can move from group to group, so it's hard to say, but we definitely, based upon our analysis with machine learning, it was more accurate the longer with disease progression. So I'd say early on, tricky, as they can move, but I think definitely we are moving in the right direction in terms of identifying these in advance.

Dr. Jaime Hatcher-Martin: And there's a kind of a couple of questions that sort of relate to this. Can you talk a little bit about so when you let's just say maybe for DBS analysis, you know, pre pre DBS, can you talk a little bit about, you know, patients like how you do that patients coming in off medication on medication, what, what are you looking 

Dr. Christine Esper: for there as part of the DBS assessment.

Sure. So in our group, we have a pretty [00:46:00] specified protocol patients. We have many movement disorder specialists. If, if a referral is placed within our group usually, you know, the, the patient will be sent to one of us as a DBS specialist to examine the patient and our protocol is that the patient needs to arrive off medications.

And what I mean by that is off Parkinson's medications for at least 12 hours and that's typically overnight. I will do a formal. Parkinson's rating scale when they arrive. I document that my notes. I we have the patients actually bring their medications. They take their medication after that, and as they are so called turning on, which can take, you know, between 15 minutes up to an hour, sometimes longer, we get the full history, you know, what, what were your symptoms?

How long has this lasted? Have you [00:47:00] responded to medications? Eventually when the, Okay. patient feels as though they're on, meaning they have responded to medications, we do another rating scale. We review those side by side and determine based upon the history as well as their response to medication, whether or not we will advance them to DBS screening.

That's essentially our process. And it, you know, if they move on, there are a number of other evaluations that are included as, as part of that. 

Dr. Jaime Hatcher-Martin: And then you touch on this a little bit with mentioning multiple sclerosis, some of the midbrain injuries obviously we have a big ALS population to are there other disorders, neurological disorders that either you guys or, you know, anybody else with motion analysis has been really helpful in either differentiating diagnosing predicting anything else that you're 

Dr. Christine Esper: aware of.

Yeah, so I. I actually had this as part of my talk, but I removed it. One of the [00:48:00] things that's often sent to us is unusual tremors, or what we call like a non physiologic tremor, a tremor that is not doesn't meet criteria for true movement disorder. And we will get those referrals even from our own group.

As well as from outside like general neurology. So we can help distinguish between those simply by the, the volume of data that we receive and I actually do I modify the protocol to do some distraction maneuvers to test for that. Some other common uses of the motion capture lab probably beyond what we're doing in our lab is for stroke.

I didn't add as much of that in this talk, but stroke and rehab, and there are a number of studies that look at even the impacted side and the use of AFOs and, and how does it help with the movements and rehabilitation. There's also [00:49:00] some studies on, on MS. I don't know of any specific ones on ALS, but I haven't.

You know, also looked for them. So I mean, that may be out there. 

Dr. Jaime Hatcher-Martin: Yeah, it'd be interesting. I think, you know, in a lot of cases And I mean, obviously, you know this, but sometimes people are unaware of their symptoms or unaware of the severity of their symptoms or when symptoms come and go, right? It's hard sometimes when you're in the midst of it.

So it'd be interesting to be able to, you know, that's a lot of what we do is when right, trying to see when patients say, I don't have any benefit from my medication, but you can clearly show in motion analysis, there is a reduction in tremor, an increase in how quickly they can walk. It'd be interesting to see that too.

And other diseases, right? Impact for medications for multiple sclerosis or for ALS to be able to show those objective measures instead of just the 

Dr. Christine Esper: yeah, sure. And even like peripheral neuropathy. I mean, there's a number of uses that we can apply this to. 

Dr. Jaime Hatcher-Martin: Absolutely. So a couple [00:50:00] of other questions. So is anybody using motion capture for clinical trial data at this point?

Dr. Christine Esper: There are some studies, I don't know if they're technically enrolled in clinical trials where there's been funding involved, if that's what they're asking. We actually have submitted something to the Michael J. Fox Foundation, and it looks like it will be funded, probably will be starting. Late fall to early winter.

This is looking more at freezing of gait. As I mentioned I know there's some groups in Oregon that are doing some work. But I don't know, like the specific inclusion and exclusion criteria, 

Dr. Jaime Hatcher-Martin: right? Right. I think the question was more just, you know, is this something that can be used in clinical trials to help again with sort of more objective measures?

Dr. Christine Esper: Yeah, I think so. 

Dr. Jaime Hatcher-Martin: And another question. So a question I commonly get right. Is there is there an [00:51:00] overlap between essential tremor and Parkinson's disease and right for the most part? No, but there are definitely some families or some cases where people can have clear essential tremor for a period of time and then At some point later develop Parkinson's and you know, the question is, is, is it truly related or is it just common things being common?

Do you know, are there any studies where people have looked at things like that? Is there, is there something in essential tremor that would predict later? I wouldn't, I don't want to say progression, but later development 

Dr. Christine Esper: of Parkinson's. So we, we looked at this pretty extensively as part of my CDC work.

And as you know, Dr. Hatcher Martin, there is some overlap. There actually can be up to 20% overlap, whether or not it's genetic or there's a relationship that is unclear. However, it can get very tricky and even you and I, who are, you know, movement disorder [00:52:00] A very severe essential tremor patient will have a rest tremor and a very severe Parkinson's patient will have an action tremor.

It can get a little hairy and I will say even as part of our DBS assessment and it's, it's a whole group involved in those decisions. We often will get imaging, there's specific imaging that you, the DAT scan for Parkinson's to help clarify this. And then to make things even more complicated is when we have An essential tremor patient who then develops Parkinsonism, and truly they have both, you know, what target do we use?

So it's, I don't think there's a clear black and white answer to your question, but certainly there is overlap that we see. 

Dr. Jaime Hatcher-Martin: Yeah, I think it's interesting, you know, and I was sort of, especially my patients that have both right. How do we know which one, how do we know which one we're treating. Generally speaking, right.

And again, you know, this [00:53:00] Parkinson's disease, more rest tremor, less posture, right positions and action. essential tremor, more posture action, generally less rest. So there's sort of, you know, where's the predominance of see how motion capture might be helpful in that and sort of see where that predominance is to figure out, you know, if, if somebody is complaining about something at this time point, well, that's the predominant, you know, tremor that they're 

Dr. Christine Esper: having at that point.

We can look at anatomy, frequency, proximal versus distal, which can be very helpful. And I will say, I mean, the good news is even a central tremor patients that are severe enough to end up getting DBS, they get a target, the IM and the thalamus, I've had a couple of patients who subsequently develop Parkinsonism, and it can help control the tremor, it will not control the other symptoms, but you know there is some hope.

And I've yet to have a patient get a second target although I'm [00:54:00] heading in that direction with somebody. So it can happen. 

Dr. Jaime Hatcher-Martin: Exciting. Okay, so I'm just going through the questions so you can assist with diagnosis or exclude other diagnoses. You mentioned that with some of the tremors that don't quite fit the standard diagnoses that we see.

Let's see. So, I'm not sure if you're aware of this. My, my suspicion is no, but are there any differences in, you know, motion analysis and people who have genetic forms? So maybe lurk to versus, you know, other quote unquote, you know, garden variety or idiopathic Parkinson's understand understanding that lurk too often just looks like.

You know, regular Parkinson's, but as far as you know, any difference with the genetic 

Dr. Christine Esper: forms in the motion testing. I wouldn't have enough data to answer that. But clinically, yes, I think we can spot those people a little bit earlier. [00:55:00] 

Dr. Jaime Hatcher-Martin: I think that covers most of the questions. You, like I said, you touched a lot of these already.

Measuring balance. Okay. So I guess maybe as a final question, because we're, you know, you're, you're both in very right. This is very involved in person evaluation, but you're also right chair of the telemedicine committee for for movement disorders. How do you see, like, how can we Make this in the long run.

How can we make a lot of these features more available for either smaller you know, practices to be able to use some of this information or, you know, for patients to be able to maybe, you know, evaluate some of these things at home. So maybe you can talk a little bit about maybe like remote patient monitoring, things like that.

Dr. Christine Esper: So, yeah, our procedure is fairly I mean, you need to be in person. We have to put that tight outfit on, put the markers on. [00:56:00] We are looking at adding some sensors now in the lab in specific areas and to see how that information correlates with our findings. Depending on that, I think there, there will be an ability to send some sensors home obviously, you know, with some restrictions and guidelines, but I think that will be the way of the future, because.

I mean, not only is that more convenient, but it's also it will capture movements, not, not just a segment in the clinic, right? I mean, you can monitor patients over time with medication status. So I think that's likely where we're heading. It's probably years from now, but that's what I anticipate. 

Dr. Jaime Hatcher-Martin: It would be, I think it'd be a cool way to, to help sort of screen people and then figure out of those who would benefit from more formal 

Dr. Christine Esper: motion.

Yeah. I mean, the question is how, [00:57:00] what subtle findings can you identify ahead of time, either for very early PD or like preclinical PD? And I think that's, That's that's really what we'd like to get to know. Yeah. 

Dr. Jaime Hatcher-Martin: And obviously very interested in in telemedicine, but I think a lot of what you presented today really highlights the importance of also having an in person.

You know, physician that can help evaluate symptoms and and touch on a lot of these things because there are things we can't do over telemedicine right now. So you know, we always tell people it's it's really good to have somebody in person to be able to evaluate all these things. And then obviously.

If you get to the point where you might need advanced therapies, DBS, focus ultrasound, then obviously that needs to be done in person as well. 

Dr. Christine Esper: Yeah, I mean, certainly I would, there was a flurry of evaluations of referrals I get around the pandemic because they were telemedicine. And they're like, Hey, Christine, you know, clarify after why I say, listen, bring you, bring the patient in.

If you're moving to our specialist, you [00:58:00] can then, if you have a question, bring them to the lab, because it's not a substitute for that, but it certainly yields a lot more information than what you and I would even see in clinic. It's very interesting. And the balance. Component is really exciting for us. I mean, we're, we already have gathered the data.

The engineers are just working on the output, but I think that will be very informative just for clinical decision making, even for the DBS team. 

Dr. Jaime Hatcher-Martin: Absolutely. Okay. Well, we are right at time. Thank you so much for doing this. This is great. We will have a recording of this later for anybody else that wants to go back and watch it and I'll make sure I share it with you.

And if anybody has any additional questions, feel free to send them over and we'll try to get them answered after today. 

Dr. Christine Esper: Thank you very much for 

Dr. Jaime Hatcher-Martin: inviting me. Thanks so much. Have a great weekend, everyone. You too. Bye bye.