Continuous and Unconstrained Tremor Monitoring in Parkinson's Disease Using Supervised Machine Learning and Wearable Sensors.

Background: Accurately assessing the severity and frequency of fluctuating motor symptoms is important at all stages of Parkinson's disease management. Contrarily to time-consuming clinical testing or patient self-reporting with uncertain reliability, recordings with wearable sensors show promise as a tool for continuously and objectively assessing PD symptoms. While wearables-based clinical assessments during standardised and scripted tasks have been successfully implemented, assessments during unconstrained activity remain a challenge. Methods: We developed and implemented a supervised machine learning algorithm, trained and tested on tremor scores. We evaluated the algorithm on a 67-hour database comprising sensor data and clinical tremor scores for 24 Parkinson patients at four extremities for periods of about 3 hours. A random 25% subset of the labelled samples was used as test data, the remainder as training data. Based on features extracted from the sensor data, a Support Vector Machine was trained to predict tremor severity. Due to the inherent imbalance in tremor scores, we applied dataset rebalancing techniques. Results: Our classifier demonstrated robust performance in detecting tremor events with a sensitivity of 0.90 on the test-portion of the resampled dataset. The overall classification accuracy was high at 0.88. Conclusion: We implemented an accurate classifier for tremor monitoring in free-living environments that can be trained even with modestly sized and imbalanced datasets. This advancement offers significant clinical value in continuously monitoring Parkinson's disease symptoms beyond the hospital setting, paving the way for personalized management of PD, timely therapeutic adjustments, and improved patient quality of life.

Movement Reminders to Encourage Arm Use During Daily Life in Stroke Patients.

Stroke is a leading cause of long-term disability, such as loss of upper limb function. Active arm movement and frequent practice are essential to regain such function. Wearable sensors that trigger individualized movement reminders can promote awareness of the affected limb during periods of inactivity. This study investigated the immediate effect of vibrotactile reminders based on activity counts on affected arm use, the evolution of the effect throughout a 6-week intervention at home, and whether the time of the day influences the response to the reminder. Thirteen participants who experienced a unilateral ischemic stroke were included in the analysis. Activity counts were found to increase significantly after receiving a reminder. The immediate effect of receiving a reminder was maintained throughout the day as well as during the study duration of 6 weeks. In conclusion, wearable activity trackers with a feature to trigger individualized vibrotactile reminders could be a promising rehabilitation tool to increase arm activity of the affected side in stroke patients in their home environment.

A novel method to increase specificity of sleep-wake classifiers based on wrist-worn actigraphy.

The knowledge of the distribution of sleep and wake over a 24-h day is essential for a comprehensive image of sleep-wake rhythms. Current sleep-wake scoring algorithms for wrist-worn actigraphy suffer from low specificities, which leads to an underestimation of the time staying awake. The goal of this study (ClinicalTrials.gov Identifier: NCT03356938) was to develop a sleep-wake classifier with increased specificity. By artificially balancing the training dataset to contain as much wake as sleep epochs from day- and nighttime measurements from 12 subjects, we optimized the classification parameters to an optimal trade-off between sensitivity and specificity. The resulting sleep-wake classifier achieved high specificity of 80.4% and sensitivity of 88.6% on the balanced dataset containing 3079.9 h of actimeter data. In the validation on night sleep of separate adaptation recordings from 19 healthy subjects, the sleep-wake classifier achieved 89.4% sensitivity and 64.6% specificity and estimated accurately total sleep time and sleep efficiency with a mean difference of 12.16 min and 2.83%, respectively. This new, device-independent method allows to rid sleep-wake classifiers from their bias towards sleep detection and lay a foundation for more accurate assessments in everyday life, which could be applied to monitor patients with fragmented sleep-wake rhythms.

Fully embedded myoelectric control for a wearable robotic hand orthosis.

To prevent learned non-use of the affected hand in chronic stroke survivors, rehabilitative training should be continued after discharge from the hospital. Robotic hand orthoses are a promising approach for home rehabilitation. When combined with intuitive control based on electromyography, the therapy outcome can be improved. However, such systems often require extensive cabling, experience in electrode placement and connection to external computers. This paper presents the framework for a stand-alone, fully wearable and real-time myoelectric intention detection system based on the Myo armband. The hard and software for real-time gesture classification were developed and combined with a routine to train and customize the classifier, leading to a unique ease of use. The system including training of the classifier can be set up within less than one minute. Results demonstrated that: (1) the proposed algorithm can classify five gestures with an accuracy of 98%, (2) the final system can online classify three gestures with an accuracy of 94.3% and, in a preliminary test, (3) classify three gestures from data acquired from mildly to severely impaired stroke survivors with an accuracy of over 78.8%. These results highlight the potential of the presented system for electromyography-based intention detection for stroke survivors and, with the integration of the system into a robotic hand orthosis, the potential for a wearable platform for all day robot-assisted home rehabilitation.