Triaxial rehabilitative data analysis incorporating matching pursuit

Abstract

The continuing drive for better rehabilitative healthcare hinges on the availability of sensor data which can be shared and analysed. This leverages on the w idespread communications network to provide an integrated health management environment. For this paper, we delineate our current work in sensorizing rehabilitative tests of upper limb movements . Where previously w e applied data driven analysis, we now employ time-frequency methods to provide a better analytical basis for our derivations. The use of Matching Pursuit algorithm in biological signals has concentrated on brain signals and much less on human motion. Thus we contribute to efficacy of the algorithm by employing it on rehabilitative data collected from widely available sensors. We describe how w e obtained the parameters based on pre-analysing an available data set. By selecting the most useful signal constituents and applying this to signal denoising, we are able to better classify the condition of a patient automatically -which shows encouraging promise in the quest for integrative healthcare.

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

“…In this category, systems that aim to quantify the level of correctness in executing the prescribed exercises are identified. Researchers achieved this by using popular post-stroke assessment scoring systems [65][66][67][68][69][70][71][72][73][74]:…”

“…Task performance is rated on a 4-point scale, ranging from 0 (no movement) to 3 (movement performed normally). Two of the included works used the ARAT system [66,72] Oxford Grading Motor-Scale (OGM) [81] used in a single study included [70], it evaluates the muscle strength of the rehabilitated patient and can help diagnose problems in which weakness plays a role. It is not proper to stroke rehabilitation and targets both upper and lower extremities.…”

“…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].…”