Role of data measurement characteristics in the accurate detection of Parkinson’s disease symptoms using wearable sensors

If we had two functioning neurons anywhere in stroke leadership they would immediately see the usefulness in this in getting objective measurements of movement disabilities in stroke. And then assign research to correct those disabilities. But we have NO LEADERSHIP. We do have idiots that spout the damnable idea; 'All strokes are different, all stroke recoveries are different'. Anyone using that is too lazy to read stroke research and see a way forward. 

Role of data measurement characteristics in the accurate detection of Parkinson’s disease symptoms using wearable sensors

• Nicholas Shawen,
• Megan K. O’Brien,
• Sanjeev Venkatesan,
• Luca Lonini,
• Tanya Simuni,
• Jamie L. Hamilton,
• Roozbeh Ghaffari,
• John A. Rogers &
• Arun Jayaraman 

Journal of NeuroEngineering and Rehabilitation volume 17, Article number: 52 (2020) Cite this article

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Abstract

Background

Parkinson’s disease (PD) is a progressive neurological disease, with characteristic motor symptoms such as tremor and bradykinesia. There is a growing interest to continuously monitor these and other symptoms through body-worn sensor technology. However, limited battery life and memory capacity hinder the potential for continuous, long-term monitoring with these devices. There is little information available on the relative value of adding sensors, increasing sampling rate, or computing complex signal features, all of which may improve accuracy of symptom detection at the expense of computational resources. Here we build on a previous study to investigate the relationship between data measurement characteristics and accuracy when using wearable sensor data to classify tremor and bradykinesia in patients with PD.

Methods

Thirteen individuals with PD wore a flexible, skin-mounted sensor (collecting tri-axial accelerometer and gyroscope data) and a commercial smart watch (collecting tri-axial accelerometer data) on their predominantly affected hand. The participants performed a series of standardized motor tasks, during which a clinician scored the severity of tremor and bradykinesia in that limb. Machine learning models were trained on scored data to classify tremor and bradykinesia. Model performance was compared when using different types of sensors (accelerometer and/or gyroscope), different data sampling rates (up to 62.5 Hz), and different categories of pre-engineered features (up to 148 features). Performance was also compared between the flexible sensor and smart watch for each analysis.

Results

First, there was no effect of device type for classifying tremor symptoms (p > 0.34), but bradykinesia models incorporating gyroscope data performed slightly better (up to 0.05 AUROC) than other models (p = 0.01). Second, model performance decreased with sampling frequency (p < 0.001) for tremor, but not bradykinesia (p > 0.47). Finally, model performance for both symptoms was maintained after substantially reducing the feature set.

Conclusions

Our findings demonstrate the ability to simplify measurement characteristics from body-worn sensors while maintaining performance in PD symptom detection. Understanding the trade-off between model performance and data resolution is crucial to design efficient, accurate wearable sensing systems. This approach may improve the feasibility of long-term, continuous, and real-time monitoring of PD symptoms by reducing computational burden on wearable devices.