Quantitative Measurement of Bradykinesia in Parkinson's Disease

using Commercially Available Leap Motion

Yusuf Özgür Çakmak

, S. Can Ölçek

, Burak Özsoy

and Didem Gökçay

Department of Anatomy, University of Otago, Dunedin, New Zealand

Department of Health Informatics, Middle East Technical University, Graduate School of Informatics, Ankara, Turkey

Global Dynamic Systems (GDS) ARGE, Teknopark Istanbul, Istanbul, Turkey

Keywords: Parkinsons, Bradykinesia, Updrs, Quantitative, Leap Motion, Pinching.

Abstract: Parkinson’s Disease (PD) is a neurodegenerative disease caused by the depletion of dopamine in the brain.

Tremor, bradykinesia, rigidity and postural stability are the four major symptoms. Like other symptoms,

bradykinesia causing unnatural stillness/slowness in motions affects the daily life of the patients. The levels

of these symptoms are clinically assessed by a scoring system based on Unified Parkinson's Disease Rating

Scale (UPDRS). However, UPDRS relies on the visual observations of physicians rather than a test based

on quantitative measurements. This makes it not only difficulty to repeat but also subjective. Because of

these two major disadvantages, researchers build custom devices for their studies. But this leads to the

reliability issues and non-standard measurements. Thus, 24 PD patients were bilaterally UPDRS III (motor

subsection) scored and recorded for finger motion (pinching) by using commercially available off-the-shelf

(COTS) product called Leap Motion. The various features extracted from recordings and UPDRS III scores

were analyzed for correlation. After the analysis, a linear model was created to estimate UPDRS III score.

The study revealed that Leap Motion, a COTS device, can be used to estimate bradykinesia of a patient with

PD.

1 INTRODUCTION

Bradykinesia which results in unnatural

stillness/slowness in the motions is one of the early

symptoms of Parkinson's Disease (PD). Together

with tremor, rigidity, and postural instability, they

are named as four cardinal symptoms of the disease

(Calne et al., 1992). The main cause of bradykinesia

is the dopamine deficiency in basal ganglia from

which the inhibitory signals are sent to the motor

systems to prevent involuntary actions. Under

normal circumstances when the dopamine is present,

basal ganglia promotes those motor actions so that

the body can act swiftly (Blandini et al., 2000).

Because of further depletion of dopamine in later

stages, bradykinesia follows the progression of the

disease and it gets worse.

The level of disease and its symptoms are

evaluated by Unified Parkinson's Disease Rating

Scale (UPDRS). UPDRS scoring, which is based on

the observations of the physician conducting it, is

the main clinical approach to diagnose and assess

the progression of the disease. Even though UPDRS

III (motor subsection) covers almost all the aspects

of the motor symptoms (Fahn et al., 1987) it depends

on the subjective scoring of the physicians. In

addition to this inconsistency, the discreet rating

scale cannot detect the subtle changes in the

symptoms such as bradykinesia. Therefore, UPDRS

solely is not adequate for research or treatment of

PD.

Various researchers (Dunnewold et al., 1997;

Salarian et al., 2007; Kandori et al., 2004; Ghassemi

et al., 2006; Sande de Souza et al., 2011; Marsili et

al., 2014; Daneault et al., 2013) have tried many

different assessment techniques to overcome the

inadequacy of UPDRS for detecting bradykinesia.

All these techniques are mostly focused on rapid

alternating movements (RAM) or finger

tapping/pinching. For example, Dunnewold et al.

(1997) used tap rate (TR) and movement time (MT)

to assess slowness in the motion. Similarly,

Ghassemi et al. (2006) used another RAM which is

pronation-supination action to measure bradykinesia.

However, in Ghassemi et al.’s work, the pronation-

supination action did not show a significant

Çakmak, Y., Ölçek, S., Özsoy, B. and Gökçay, D.

Quantitative Measurement of Bradykinesia in Parkinson’s Disease using Commercially Available Leap Motion.

DOI: 10.5220/0006655402270232

In Proceedings of the 11th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2018) - Volume 4: BIOSIGNALS, pages 227-232

ISBN: 978-989-758-279-0

227

correlation with the bradykinesia level unlike the

tapping and alternating hand movements used in

other studies. Nonetheless, Daneault et al. (2013)

clarified those odd findings by showing that the

maximal and mean velocity of pronation-supination

cycles has significant correlation rather than the

cycle duration. Even though all the studies agree that

RAM based tasks can be used in assessing

bradykinesia level, the measurement techniques are

relying on wide variety of devices such as

accelerometers (Dunnewold et al., 1997),

gyroscopes (Salarian et al., 2007), magnetic devices

(Kandori et al., 2004; Ghassemi et al., 2006), and

EMG sensors (Sande de Souza et al., 2011). The

common problem of all these devices is that they are

depending on custom designs or setups. In other

words, they are not commercially available off-the-

shelf (COTS) products.

The objective of this study is to develop a new

method to measure bradykinesia in PD patients by

using COTS product called Leap Motion. Thus, the

efficiency of Leap Motion is studied by recording

various motor tasks performed by PD patients. The

recorded data is analyzed for its various features

against the UPDRS scores. The aim is to be able to

utilize this easily available and relatively cheap

device for daily tracking of patients and their

treatments. The study is approved by the local Ethics

Committees of Koç University Hospital, İstanbul,

Turkey and all participants gave informed consent

prior to the study.

2 METHODS

2.1 Measurement Device

Leap Motion (Leap Motion, Inc., San Francisco,

USA) is a motion controller device to capture hand

gestures by using pair of cameras and infrared

lighting. It is a fairly compact device and very

powerful to capture obvious hand motions like

pinching and pronation/supination. Figure 1 shows

the device interior and its compact design.

Figure 1: The representation of interior design of Leap

Motion taken from its product page.

Weichert et. al. (2013) analyzed the accuracy of

leap motion controller and found that it can achieve

0.7 mm overall average accuracy in all 3 axes. This

result is comparable to the average human hand

accuracy, 0.4 mm. Besides the accuracy, the

controller is able to sample the hand motions around

100 Hz.

2.2 Recorded Motor Tasks and

Features

Pinching and Pronation-Supination are the two

motor tasks given to the subjects. In this study, we

will report preliminary results from the pinching task

only. Other data will be reported separately. With

the software developed on top of Leap Motion SDK,

the positions and rotations of the finger joints and

wrist are recorded during these tasks. After the

recording session, the raw data is processed and

several features are extracted. For the pinching, the

local minima and maxima of the distances between

thumb and index finger are marked. Afterwards, the

time difference between the consecutive minimum

and maximum is calculated.

By using the time difference and distance

obtained from the raw data processing, the speed,

acceleration, and frequency of a motion are

calculated. In previous studies, it was shown that

those three measures can be used to assess

bradykinesia. (Dunnewold et al., 1997; Daneault et

al., 2013)

2.3 Subjects and Experiment Protocol

24 patients (7 female, 17 male, mean age ± SD =

57.08 ± 8.91) who were diagnosed by neurologist

for PD participated in the experiment. All patients

were under dopaminergic replacement treatment and

their disease duration was 8.04 ± 3.88 years. 20

patients were right-handed whereas 4 patients were

left-handed. They came to the hospital in 12-hour

OFF state (without medication) and two independent

neurologists immediately evaluated UPDRS III

(motor section) bilaterally. The average of those two

scorings was considered as the final bilateral

UPDRS scores (



 



  





 



  ). The patients

were not specifically marked as tremor or

bradykinesia dominant.

The patients visited hospital multiple times for

another ongoing study for the data acquisition. There

was at least one week difference between visits. 9

patients came to hospital twice and 15 remaining

patients were recorded three times. In every case, the

BIOSIGNALS 2018 - 11th International Conference on Bio-inspired Systems and Signal Processing

228

patients were seated against a laptop computer to

which the leap motion controller is connected. The

controller was laid on the table. To familiarize the

patients with the device and to test the setup, they

were asked to put their hand above the controller and

move their fingers as shown in Figure 2. It was

visually verified that the controller was capturing the

gestures.

Figure 2: Basic recording setup with laptop and leap

controller.

After the initial UPDRS scoring and familiariza-

tion was completed, the participant started to

experiment. During the study, the motor tasks given

to the patients were recorded in 3 successive

sessions for both hands. Namely, one patient has

total 12 recordings (6 pinching, 6 wrist motion) per

hospital visit. At the end of the data acquisition

phase, total 378 recordings were taken for pinching.

The important part of the study is that before each

session, bilateral UPDRS III scoring was evaluated

by the same neurologists. The reason for the

repeated scoring is to capture the subtle changes in

the symptoms between the visits and different

sessions. Each motion task was recorded at least 10

seconds for both hands one after another.

2.4 Analysis and Statistics

Regardless of the session and action hand, the

feature extraction was applied onto all recordings.

Because of the fixation problems observed in the

data (Figure 3), the first several extracted values of

each feature (time difference, distance, and angles)

were removed. With remaining features, the mean

and standard deviation of speed, acceleration, and

frequency were calculated. By comparing the mean

and standard deviation of each metric, it was decided

if the patient could perform the task correctly or not.

Table 1 lists several exemplary values discarded

because of having large deviations. In other words,

the examples in the table have SD values which are

almost comparable to the corresponding mean

values.

Figure 3: Change of the distance between thumb and index

finger during pinching for 3 different patients. The

fixation problem can be seen at the start (before 2 seconds)

of signal where the pattern is distorted.

Table 1: Discarded speed values because of large SD.

Mean Value (mm/s)

SD (mm/s)

216.80

129.36

689.53

543.78

Since the bilateral UPDRS scores were

independently taken before each session, the values

calculated for both hands were pooled together as

Marsili et al. (2014) did. Similarly, the recordings of

all the visits and their three distinct sessions were

also combined. This data pooling process was done

separately for each motor task. After obtaining the

Quantitative Measurement of Bradykinesia in Parkinson’s Disease using Commercially Available Leap Motion

229

two big sets of recordings, the correct metric was

selected for pinching and pronation-supination,

respectively. Thus, Pearson’s correlation was

applied between UPDRS scores and three metrics

derived from extracted features.

Later, by using all the metrics of both motor

tasks, a linear regression model as in Equation 1 was

derived to improve the link between UPDRS III and

the data gathered from the controller. The

correctness of the model was evaluated by the root-

mean-square error defined by Equation 2.













  







 

(1)

















 













(2)

3 RESULTS

Some patients couldn’t complete the tasks given to

them. There were 9 such sessions that were excluded

from the study. Unrelated to the data content, the

data belonging to one patient were discarded

because of invalid UPDRS scoring. The features of

43 pinching recordings couldn’t be extracted

because of invalid or missing data. As a result, these

43 data were also removed from the data pool.

The investigation of mean and standard deviation

of metrics calculated for remaining sessions revealed

that almost half of the data for each metric have

large deviations ( 

 ). Since it is not possible

to include these inconsistent values, the correlation

study was completed by discarding them.

Firstly, the pinching task was analyzed and it

was found that there were very low correlations

(







 





) between the pinching metrics and their

respective contra-lateral UPDRS III scores.

However, when the analysis was conducted against

the ipsi-lateral scores, a moderate correlation was

obtained (













 ). UPDRS III motor section

contains many items focusing on a specific

symptom. Thus, the correlation study was repeated

against the bradykinesia subset of UPDRS III

because the pinching performance should be mostly

affected by bradykinesia. As expected, the results

(







 





) got better for all three metrics. In the

end, the speed is the best metric for the pinching.

Even though the speed was selected as the best

metric for pinching, the values were fitted to create

linear model from all metrics as in Equation 3 (

  ) to

estimate UPDRS III score.













 







 







 

(3)













(4)

The correlation between pinching and

bradykinesia was significant so should be the linear

model when the features of pinching is selected as

sole predictors. The important point is that this

model had small root-mean-square error (





) for estimating total UPDRS III score. To

better visualize the error, it is normalized (





) by the max value of UPDRS III as in

Equation 4.

Because of stronger correlation with

bradykinesia subset in pinching, the linear model

was also created for UPDRS III bradykinesia score.

As expected, the error of this model was similarly

small (



 



). Even though the

normalized value was slightly bigger than the error

in the total score case, it was not significantly

different.

Instead of using whole data to create the model,

the training procedure was repeated by using

randomly selected 75% of the data. After training,

the remaining 25% of the data was used for testing

the model. This training-testing procedure was

repeated 100 times for the different randomly

selected training set. After 100 repetitions, the

average RMSE values were calculated. The results

of trained model were similar to the previous

approach for both total UPDRS III (









 and bradykinesia subset (









cases. The important finding was that

error of estimations was (



 





and (



 



 respectively.

4 DISCUSSIONS

In this study, we showed that a COTS device can be

used in simple setup to assess the bradykinesia level

of the patient with PD. Furthermore, it is important

that the assessment was done by using a quantitative

metric acquired from the device. By comparing the

measurements with the UPDRS III scores which are

based on the subjective observations of physicians, it

was seen that this method can be used as a fast and

BIOSIGNALS 2018 - 11th International Conference on Bio-inspired Systems and Signal Processing

230

Figure 4: Distribution of UPDRS III total score and bradykinesia subset against the three metrics (speed, acceleration,

frequency) extracted from the pinching recordings. The metrics are the combination of data from all the session for both

hands. The upper two rows show the ipsi-lateral results while the bottom two rows belong to contra-lateral results. As

expected, the speed showed the highest correlation (  and the correlation ( 

 increases by using bradykinesia subset. Furthermore, the contra-lateral analysis revealed that there was no

correlation between the total ( ) and the metrics. Even using bradykinesia subset ( 

 scores did not improve it.

reliable alternative. The main advantage of this

technique is that it helps the physician by keeping

the process completely objective, thus, they can

better decide on treatment regime. Nevertheless, the

number of invalid data suggested that the patients

need further familiarization with the task and device.

This can be overcome by extending the recording

time and the familiarization time. The exclusion of

data could be done by using z-scores of the metrics

which might give further information why the

patients couldn’t complete the given task.

UPDRS is a subjective scoring system, although

it is widely used in the clinic. Due to its subjectivity,

having mild to moderate correlations of UPDRS

Quantitative Measurement of Bradykinesia in Parkinson’s Disease using Commercially Available Leap Motion

231

with an actual physical measure is not surprising.

Despite this fact, UPDRS III was chosen for the

validation because it is the clinical golden standard

for diagnosis and prognosis. The correlation study

revealed that the fine movements like pinching

expresses bradykinesia well. Further testing of linear

model showed that this method is less error-prone

than the UPDRS. If a physician makes 1 scale-unit

error for each item, the error becomes 





which is a value much larger than our proposed

model’s error.

5 CONCLUSION

The study proved that a commercially available

cheap Leap Motion device can be used to measure

bradykinesia level from simple motor tasks. In

comparison to UPDRS scoring relying on the

physicians’ observations, it provides repeatable and

quantitative measurements. These two major

advantages of technique make it suitable for research

purposes where the detection of subtle changes in

symptoms is required. The possibility of using a

COTS device can be an invaluable asset for other

researchers. With further investigations such as

comparison with the results of another clinical

physiologic sensor, Leap Motion can be converted to

the household self-assessment device. Unfortuna-

tely, in our study, the data exclusion rate was high,

which calls for attention to investigate further the

applicability of this procedure in the clinic.

REFERENCES

Blandini, F., Nappi, G., Tassorelli, C. and Martignoni, E.

(2000). Functional changes of the basal ganglia

circuitry in Parkinson's disease. Progress in

Neurobiology, 62(1), pp.63-88.

Calne, D., Snow, B. and Lee, C. (1992). Criteria for

diagnosing Parkinson's disease. Annals of Neurology,

32(S1), pp.S125-S127.

Daneault, J., Carignan, B., Sadikot, A. and Duval, C.

(2013). Are quantitative and clinical measures of

bradykinesia related in advanced Parkinson's disease?.

Journal of Neuroscience Methods, 219(2), pp.220-223.

Dunnewold, R., Jacobi, C. and van Hilten, J. (1997).

Quantitative assessment of bradykinesia in patients

with Parkinson's disease. Journal of Neuroscience

Methods, 74(1), pp.107-112.

Fahn, S., Marsden, C., Goldstein, M. and Calne, D.

(1987). Recent developments in Parkinson's disease.

Volume 2. Florham Park: Macmillan Healthcare

Information, pp.153–163.

Ghassemi, M., Lemieux, S., Jog, M., Edwards, R. and

Duval, C. (2006). Bradykinesia in patients with

Parkinson's disease having levodopa-induced

dyskinesias. Brain Research Bulletin, 69(5), pp. 512-

518.

Kandori, A., Yokoe, M., Sakoda, S., Abe, K., Miyashita,

T., Oe, H., Naritomi, H., Ogata, K. and Tsukada, K.

(2004). Quantitative magnetic detection of finger

movements in patients with Parkinson’s disease.

Neuroscience Research, 49(2), pp.253-260.

Marsili, L., Agostino, R., Bologna, M., Belvisi, D., Palma,

A., Fabbrini, G. and Berardelli, A. (2014).

Bradykinesia of posed smiling and voluntary

movement of the lower face in Parkinson's disease.

Parkinsonism & Related Disorders, 20(4), pp. 370-

375.

Salarian, A., Russmann, H., Wider, C., Burkhard, P.,

Vingerhoets, F. and Aminian, K. (2007).

Quantification of Tremor and Bradykinesia in

Parkinson's Disease Using a Novel Ambulatory

Monitoring System. IEEE Transactions on Biomedical

Engineering, 54(2), pp.313-322.

Sande de Souza, L., Dionísio, V. and Almeida, G. (2011).

Multi-joint movements with reversal in Parkinson’s

disease: Kinematics and electromyography. Journal of

Electromyography and Kinesiology, 21(2), pp.376-

383.

Weichert, F., Bachmann, D., Rudak, B. and Fisseler, D.

(2013). Analysis of the Accuracy and Robustness of

the Leap Motion Controller. Sensors, 13(5), pp.6380-

6393.

BIOSIGNALS 2018 - 11th International Conference on Bio-inspired Systems and Signal Processing

232