Measuring Activities of Daily Living in Stroke Patients with Motion Machine Learning Algorithms: A Pilot Study

Abstract

Measuring activities of daily living (ADLs) using wearable technologies may offer higher precision and granularity than the current clinical assessments for patients after stroke. This study aimed to develop and determine the accuracy of detecting different ADLs using machine-learning (ML) algorithms and wearable sensors. Eleven post-stroke patients participated in this pilot study at an ADL Simulation Lab across two study visits. We collected blocks of repeated activity (“atomic” activity) performance data to train our ML algorithms during one visit. We evaluated our ML algorithms using independent semi-naturalistic activity data collected at a separate session. We tested Decision Tree, Random Forest, Support Vector Machine (SVM), and eXtreme Gradient Boosting (XGBoost) for model development. XGBoost was the best classification model. We achieved 82% accuracy based on ten ADL tasks. With a model including seven tasks, accuracy improved to 90%. ADL tasks included chopping food, vacuuming, sweeping, spreading jam or butter, folding laundry, eating, brushing teeth, taking off/putting on a shirt, wiping a cupboard, and buttoning a shirt. Results provide preliminary evidence that ADL functioning can be predicted with adequate accuracy using wearable sensors and ML. The use of external validation (independent training and testing data sets) and semi-naturalistic testing data is a major strength of the study and a step closer to the long-term goal of ADL monitoring in real-world settings. Further investigation is needed to improve the ADL prediction accuracy, increase the number of tasks monitored, and test the model outside of a laboratory setting.

Wearable sensors and machine learning in post-stroke rehabilitation assessment: A systematic review

“…Are systems which aim to identify specific movements of rehabilitation of the patients and differentiate between them for record and monitoring purposes [41][42][43][44][45][46][47][48][49][50][51], in this category researchers monitored Activities of Daily Living (ADL) [75] and they most frequently covered detecting general activities like standing, sitting, lying, standing up, sitting down [42,44,47,48,50], performing kitchen tasks like making a drink, chopping food [42] and other routine activities like making the bed, reading and lacing shoes [48], folding, sweeping and brushing teeth [46,48,49]. Other researchers covered activities for specific body parts like recognising different hand gestures [41], arm gestures [43] and some exercises to strengthen shoulders, and arms [48].…”

“…Over the past few years, effort has been put into developing unobtrusive, effective and objective motion-modeling systems, taking advantage of the progress made in the sensor technology which became more compact and more power-efficient [83]. All the included works utilised IMUs for the data acquisition [42][43][44][45][46][47][48][49][50][52][53][54][55][56][57][58][59][60][61][65][66][67][68][69][70]63,[71][72][73][74]64]. IMUs are devices that combine linear acceleration from accelerometer and the angular turning rates from gyroscopes [84].…”

“…Placement of the sensing technology on the body has shown a heterogeneous distribution linked to the different nature of the employed technology and to the purpose for which the monitoring system was designed. Systems that focused on upper-limb rehabilitation used more frequently the wrists [46,49,52,53,58,65,68,69,[71][72][73][74] in twelve studies, arms [43,49,54,58,61,67,69,70,63,64] in ten studies, four studies used forearm [41,66,63,64], three studies used fingers [53,57,68], and hands [42,45,64], and a single study used elbow and shoulder [68]. These placements were targeted to monitor activities that involved using hands.…”

“…Other studies monitored participants in an outpatient home environment [63]. For the study population, many works recruited stroke survivors with different degrees of severity after getting ethical approvals [43][44][45][46][47][48][49]52,54,[56][57][58][59][60][66][67][68][69][70]63,[71][72][73][74]64]. Some of them undertook a cohort study by combining them with Able Bodied (AB) participants [43][44][45][46]52,57,58,60,67,64] elderly [52] and neurologically disordered patients [59].…”

Upper extremity sensory training after stroke

“…Sensation is essential for safety even if there is adequate motor recovery [11]. Findings particularly suggest the importance of somatosensory function after stroke for recovery of precision grip force control [12], safety and dexterity in the paretic hand [13] and functional independence in activities of daily living (ADL) [14,15]. Current findings showed that active and passive sensory retraining may be an effective intervention for improving the light touch threshold of the hand, dexterity, upper extremity (UE) motor function [10,16] to improve the activity of daily living in stroke patients with impaired sensory motor abilities [17].…”