Quantification of Bradykinesia Using Gyro-Sensors during Clinical Motor Examination in Idiopathic Parkinson’s Disease Patients

Kim, J. W.; Kwon, Yuri; Eom, Gwang-Moon; Kwon, Do Young; Koh, Seong Beom; Park, B. K.

doi:10.1007/978-3-642-14515-5_326

Cited by 2 publications

(3 citation statements)

References 7 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Numerous studies have attempted to overcome the drawbacks of subjective methods. Existing works have been conducted with vision, 8 Wi-Fi 9 and wearable devices [10][11][12] to aid the doctors' rating. However, they still have shortcomings in privacy, medical ethics, costs and hygiene.…”

Section: -Moderatementioning

confidence: 99%

“…Prateek et al 11 utilized inertial sensors to detect and track freezing of gait in PD. J.W.Kim et al 12 use gyro‐sensors to achieve quantification of Bradykinesia during Clinical Motor Examination in Idiopathic PD Patients. Hanbin Zhang et al 26 propose a system named “PDMove” to achieve passive medication adherence monitoring of PD patients with smartphones.…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

PD‐Gait: Contactless and privacy‐preserving gait measurement of Parkinson's disease patients using acoustic signals

Li,

Pan,

Dai

et al. 2023

Softw Pract Exp

View full text Add to dashboard Cite

In this article, we propose a mobile edge computing (MEC)‐related system named PD‐Gait, which can measure gait parameters of Parkinson's disease patients in a contactless and privacy‐preserving manner. We utilize inaudible acoustic signals and band‐pass filters to achieve privacy data protection in the physical layer. The proposed framework can be easily deployed in the mobile end of MEC, and hence release the edge server in cybersecurity attacks fighting. The gait parameters include stride cycle time length and moving speed, and hence providing an objective basis for the doctors' judgment. PD‐Gait utilizes acoustic signals in bands from 16 to 23 kHz to achieve device‐free sensing, which would release both doctors and patients from the tedious wearing process and psychological burden caused by traditional wearable devices. To achieve robust measurement, we propose a novel acoustic ranging method to avoid “broken tones” and “uneven peak distribution” in the received data. The corresponding ranging accuracy is 0.1 m. We also propose auto‐focus micro‐Doppler features to extract robust stride cycle time length, and can achieve an accuracy of 0.052 s. We deployed PD‐Gait in a brain hospital and collected data from 8 patients. The total walked distance is over 330 m. From the overall trend, our results are highly correlated with the doctor's judgment.

show abstract

Section: -Moderatementioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

PD‐Gait: Contactless and privacy‐preserving gait measurement of Parkinson's disease patients using acoustic signals

Li,

Pan,

Dai

et al. 2023

Softw Pract Exp

View full text Add to dashboard Cite

show abstract

“…Gyroscopes are commonly built into consumer wearable devices such as smart watches and smartphones. They are used in customised devices for clinical applications such as quantification of tremor [24] and bradykinesia [25] , [26] in Parkinson’s disease, and the recording of hand tapping angular movement [27] . Elble et al [28] investigated the use of accelerometers and gyroscopes for measuring head tremors and found gyroscopes to be more effective.…”

Section: Introductionmentioning

confidence: 99%

Wearable Accelerometer and Gyroscope Sensors for Estimating the Severity of Essential Tremor

Ali,

Arjunan,

Peter

et al. 2024

IEEE J. Transl. Eng. Health Med.

View full text Add to dashboard Cite

Background: Several validated clinical scales measure the severity of essential tremor (ET). Their assessments are subjective and can depend on familiarity and training with scoring systems. Method: We propose a multi-modal sensing using a wearable inertial measurement unit for estimating scores on the Fahn-Tolosa-Marin tremor rating scale (FTM) and determine the classification accuracy within the tremor type. 17 ET participants and 18 healthy controls were recruited for the study. Two movement disorder neurologists who were blinded to prior clinical information viewed video recordings and scored the FTM. Participants drew a guided Archimedes spiral while wearing an inertial measurement unit placed at the mid-point between the lateral epicondyle of the humerus and the anatomical snuff box. Acceleration and gyroscope recordings were analyzed. The ratio of the power spectral density between frequency bands 0.5-4 Hz and 4-12 Hz, and the sum of power spectrum density over the entire spectrum of 2-74 Hz, for both accelerometer and gyroscope data, were computed. FTM was estimated using regression model and classification using SVM was validated using the leave-one-out method. Results: Regression analysis showed a moderate to good correlation when individual features were used, while correlation was high (r 2 = 0.818) when suitable features of the gyro and accelerometer were combined. The accuracy for two-class classification of the combined features using SVM was 91.42% while for four-class it was 68.57%. Conclusion: Potential applications of this novel wearable sensing method using a wearable Inertial Measurement Unit (IMU) include monitoring of ET and clinical trials of new treatments for the disorder.

show abstract

Quantification of Bradykinesia Using Gyro-Sensors during Clinical Motor Examination in Idiopathic Parkinson’s Disease Patients

Cited by 2 publications

References 7 publications

PD‐Gait: Contactless and privacy‐preserving gait measurement of Parkinson's disease patients using acoustic signals

PD‐Gait: Contactless and privacy‐preserving gait measurement of Parkinson's disease patients using acoustic signals

Wearable Accelerometer and Gyroscope Sensors for Estimating the Severity of Essential Tremor

Contact Info

Product

Resources

About