Smart Cup to Monitor Stroke Patients Activities During Everyday Life

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].…”

Fluid Intake Monitoring Systems for the Elderly: A Review of the Literature

“…In the preliminary results, it was able to detect liquid levels at all temperatures, but had difficulty in measuring some types of liquid such as those with large remnants at the bottom of the bottle like milk suds [ 117 ]. Bobin et al also used five conductive electrode sensors placed vertically on the inner wall of the container, as depicted in Figure 4 d, to detect liquid level, along with an IMU to capture the stability of the motion for stroke rehabilitation [ 127 ]. Using a 5-class SVM (including sitting, standing, walking, stairs and drinking), the overall accuracy of the system was 94.33% and a drink class accuracy of 96.98 [ 127 ].…”

“…Bobin et al also used five conductive electrode sensors placed vertically on the inner wall of the container, as depicted in Figure 4 d, to detect liquid level, along with an IMU to capture the stability of the motion for stroke rehabilitation [ 127 ]. Using a 5-class SVM (including sitting, standing, walking, stairs and drinking), the overall accuracy of the system was 94.33% and a drink class accuracy of 96.98 [ 127 ].…”

“… Schematic diagram of various sensor layouts for each smart container category, namely ( a ) inertial [ 120 , 121 , 122 , 123 , 124 ], ( b ) load and pressure [ 125 ], ( c ) capacitive [ 126 ], ( d ) conductive [ 127 ], ( e ) Wi-Fi [ 128 ], ( f ) vibration [ 129 ], ( g ) acoustic [ 130 ], ( h ) and other level sensor [ 131 ]. …”