Activity Recognition for Persons With Stroke Using Mobile Phone Technology: Toward Improved Performance in a Home Setting

Abstract

BackgroundSmartphones contain sensors that measure movement-related data, making them promising tools for monitoring physical activity after a stroke. Activity recognition (AR) systems are typically trained on movement data from healthy individuals collected in a laboratory setting. However, movement patterns change after a stroke (eg, gait impairment), and activities may be performed differently at home than in a lab. Thus, it is important to validate AR for gait-impaired stroke patients in a home setting for accurate clinical predictions.ObjectiveIn this study, we sought to evaluate AR performance in a home setting for individuals who had suffered a stroke, by using different sets of training activities. Specifically, we compared AR performance for persons with stroke while varying the origin of training data, based on either population (healthy persons or persons with stoke) or environment (laboratory or home setting).MethodsThirty individuals with stroke and fifteen healthy subjects performed a series of mobility-related activities, either in a laboratory or at home, while wearing a smartphone. A custom-built app collected signals from the phone’s accelerometer, gyroscope, and barometer sensors, and subjects self-labeled the mobility activities. We trained a random forest AR model using either healthy or stroke activity data. Primary measures of AR performance were (1) the mean recall of activities and (2) the misclassification of stationary and ambulatory activities.ResultsA classifier trained on stroke activity data performed better than one trained on healthy activity data, improving average recall from 53% to 75%. The healthy-trained classifier performance declined with gait impairment severity, more often misclassifying ambulatory activities as stationary ones. The classifier trained on in-lab activities had a lower average recall for at-home activities (56%) than for in-lab activities collected on a different day (77%).ConclusionsStroke-based training data is needed for high quality AR among gait-impaired individuals with stroke. Additionally, AR systems for home and community monitoring would likely benefit from including at-home activities in the training data.

Fall Detection in Individuals With Lower Limb Amputations Using Mobile Phones: Machine Learning Enhances Robustness for Real-World Applications

“…Nevertheless, we observed that the peak acceleration from real-world data exceeded the range of accelerations of the in-laboratory activities. This is not surprising because prior studies also found that in-laboratory activities look different from real-world unstructured behaviors [ 30 ]. Thus, falls and activity data during natural use of the phone must be collected to build a fall-detection system that can be deployed in an everyday scenario.…”

“…Previous studies have generally used validation on non-amputee participants despite the fact that the clinical population is the real target. However, participants with mobility impairments display different movement patterns from unimpaired individuals, which can affect the accuracy of activity recognition classifiers [ 28 - 30 ]. We pursued the possibility that amputee movements during activities and simulated fall events may have been unique enough to suggest population-specific model training.…”

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

“…This is relevant when considering a system for real-world monitoring of PD. We have previously shown that models trained on activities performed in a lab do not always generalize to activities performed at home [37]. Though the study tasks were designed to approximate naturalistic behavior, it is still critical to validate the performance of the any symptom detection model during day-to-day activities in the community.…”

Activity Recognition in Individuals Walking With Assistive Devices: The Benefits of Device-Specific Models

“…One reason is that gait patterns in individuals with disabilities can be markedly different from those of able-bodied subjects [ 15 ], and the algorithms could use different sensor features to identify activities in different populations [ 9 , 29 ]. Indeed, former studies found that activity recognition models trained on a population of young able-bodied individuals generalize poorly to patient populations, such as the elderly or patients of stroke or Parkinson’s disease [ 9 , 11 - 13 ]. Our findings are in line with these results and show that additional variability can be introduced by the use of different KAFOs.…”

“…The majority of wearable- and mobile phone–based AR studies have been conducted using healthy individuals, whereas relatively fewer studies are focused on people with disabilities [ 6 ], such as those resulting from stroke [ 7 - 9 ] or Parkinson disease [ 10 , 11 ]. Some of these studies showed that a model trained on young healthy individuals will yield poor performance when used with a different population [ 9 , 11 - 13 ], including those who need an assistive device for walking [ 14 ]. These differences arise due to the fact that movements are unique to individuals, and movements in people with a disability are different from that of able-bodied individuals [ 15 ].…”