Abstract
Recent advances in wearable sensor technology and machine learning (ML) have allowed for the seamless and objective study of human motion in clinical applications, including Parkinson's disease, and stroke. Using ML to identify salient patterns in sensor data has the potential for widespread application in neurological disorders, so understanding how to develop this approach for one's area of inquiry is vital. We previously proposed an approach that combined wearable inertial measurement units (IMUs) and ML to classify motions made by stroke patients. However, our approach had computational and practical limitations. We address these limitations here in the form of a primer, presenting how to optimize a sensor-ML approach for clinical implementation. First, we demonstrate how to identify the ML algorithm that maximizes classification performance and pragmatic implementation. Second, we demonstrate how to identify the motion capture approach that maximizes classification performance but reduces cost. We used previously collected motion data from chronic stroke patients wearing off-the-shelf IMUs during a rehabilitation-like activity. To identify the optimal ML algorithm, we compared the classification performance, computational complexity, and tuning requirements of four off-the-shelf algorithms. To identify the optimal motion capture approach, we compared the classification performance of various sensor configurations (number and location on the body) and sensor type (IMUs vs. accelerometers). Of the algorithms tested, linear discriminant analysis had the highest classification performance, low computational complexity, and modest tuning requirements. Of the sensor configurations tested, seven sensors on the paretic arm and trunk led to the highest classification performance, and IMUs outperformed accelerometers. Overall, we present a refined sensor-ML approach that maximizes both classification performance and pragmatic implementation. In addition, with this primer, we showcase important considerations for appraising off-the-shelf algorithms and sensors for quantitative motion assessment.
Highlights
Wearable sensors, such as inertial measurement units (IMUs) and accelerometers, provide an opportunity for the objective, and seamless capture of human motion
In the leave-one-subject-out analysis, which addressed the possibility of within-subject dependencies, similar overall classification performances were identified (PPVs of 89% for linear discriminant analysis (LDA), 90% for support vector machine (SVM), 83% for k-nearest neighbors (KNN), and 75% for Naïve Bayes classifier (NBC))
We found that LDA and SVM had high classification success for all four primitives and had few misclassifications (Figure 2)
Summary
Wearable sensors, such as inertial measurement units (IMUs) and accelerometers, provide an opportunity for the objective, and seamless capture of human motion. Researchers have begun using this combined sensorML approach in a number of applications These include human activity recognition [1,2,3], gesture analysis [4], assessment of bradykinesia in Parkinson’s disease [5, 6], motor function assessment in multiple sclerosis [7], and differentiating between functional and non-functional arm usage in stroke patients [8, 9]. While many of these studies showcase the application of sensors and ML in clinical populations, no previous work has detailed the various hardware and software considerations for using the sensor-ML approach. With the potential for the sensor-ML approach to have widespread applicability to neurological disorders, understanding how to develop this approach for one’s own area of inquiry is paramount
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