Review on the wearable health-care monitoring system with robust motion artifacts reduction techniques

Abstract

Purpose Wearables are gaining prominence in the health-care industry and their use is growing. The elderly and other patients can use these wearables to monitor their vitals at home and have them sent to their doctors for feedback. Many studies are being conducted to improve wearable health-care monitoring systems to obtain clinically relevant diagnoses. The accuracy of this system is limited by several challenges, such as motion artifacts (MA), power line interference, false detection and acquiring vitals using dry electrodes. This paper aims to focus on wearable health-care monitoring systems in the literature and provides the effect of MA on the wearable system. Also presents the problems faced while tracking the vitals of users. Design/methodology/approach MA is a major concern and certainly needs to be suppressed. An analysis of the causes and effects of MA on wearable monitoring systems is conducted. Also, a study from the literature on motion artifact detection and reduction is carried out and presented here. The benefits of a machine learning algorithm in a wearable monitoring system are also presented. Finally, distinct applications of the wearable monitoring system have been explored. Findings According to the study reduction of MA and multiple sensor data fusion increases the accuracy of wearable monitoring systems. Originality/value This study also presents the outlines of design modification of dry/non-contact electrodes to minimize the MA. Also, discussed few approaches to design an efficient wearable health-care monitoring system.

User Perceptions of Wearability of Knitted Sensor Garments for Long-Term Monitoring of Breathing Health: Thematic Analysis of Focus Groups and a Questionnaire Survey (Preprint)

“…For instance, there is a preference for loose short sleeved T-shirts and a 69% acceptability rate for snug fitting garments but none of the participants want to wear tight fitting garments. From a technical viewpoint, a snug fit is a pre-requisite to obtain good quality signals during normal wear to minimise motion artifacts [30]. A tight fit would be required for sports activities that are more energetic and would cause major movement in the sensors' position during the activities, deteriorating signal quality.…”

Effects of IMU sensor-to-segment calibration on clinical 3D elbow joint angles estimation

“…Each of these techniques has been presented and validated individually against reference systems. However, due to the complexity of the elbow joint, as well as numerous variables affecting measurement accuracy such as skin artefacts ( Cappello et al, 2005 ; Cutti et al, 2005 ; Prabakaran and Rufus, 2022 ), misalignment between externally observed and anatomical reference frames ( de Vries et al, 2010 ; Höglund et al, 2021 ) and sensor drift ( LaViola, 2003 ), defining the best type of calibration to adopt for every real-life scenario remains challenging. Additionally, when measuring with IMU, the type of movement performed and the anatomical joint axis considered further exacerbate differences in joint angle estimation across calibrations.…”

Processing Photoplethysmograms Recorded by Smartwatches to Improve the Quality of Derived Pulse Rate Variability

“…The above-mentioned noticeable interest in the PRV signal acquisition by wearable devices and its reliable processing to extract encoded information has prompted many research groups to sophisticated work on reducing artifacts in PPG. The vast majority of these studies has been summarized in recent review articles [12][13][14][15][16][17][18]. Overall, the approaches used fall into three general groups.…”