Robust Classification of Functional and Nonfunctional Arm Movement after Stroke Using a Single Wrist-Worn Sensor Device

Quantification of the relative arm use in patients with hemiparesis using inertial measurement units

“… 42 , 43 Recent work using supervised machine learning algorithms, have shown promising results in detecting arm use. 27 , 31 – 33 Although the performance of these machine learning approaches in patients are not as good as those in healthy subjects, they still perform better than activity counting or the gross movement score algorithms. Thus, these approaches are likely to gain traction in the coming years with an increasing focus on patient-specific models.…”

“… 28 – 30 Some accelerometry based data-driven approaches using machine learning algorithms to classify functional or non-functional movements yield higher classification accuracy but are restricted to specific tasks used in the laboratory setting. 31 – 33 Other methods to accurately measure arm use require multiple sensors which can lower patient compliance, or optical tracking which are impractical for the natural settings. Hence, there is a need for wearable devices with high sensitivity and specificity to detect functional and non-functional movements, along with good generalisability to estimate arm use in natural settings using minimum number of sensors.…”

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].…”

Quantification of the relative arm-use in patients with hemiparesis using inertial measurement units

“…Quantifying arm-use by without distinguishing between functional versus non-functional arm movements while assumptions on the mobility status of patients can lead to overestimation of arm-use [19][20][21] . Some data-driven approaches using supervised or unsupervised learning algorithms to classify functional or non-functional movements through accelerometry yield high classification accuracy, but are restricted to specific tasks that were used in the laboratory setting [22][23][24] . A simple, elegant and general algorithm to detect functional arm-use of an upper-limb was proposed by Leuenberger et al using a single IMU on the forearm 25 .…”