Smart System for Recognizing Daily Human Activities Based on Wrist IMU Sensors

Ayman, Ahmed; Attalah, Omneya; Shaban, Heba

doi:10.1109/aect47998.2020.9194154

Cited by 5 publications

(4 citation statements)

References 17 publications

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“…Eligible populations include the general public as well as specific demographics including children, pregnant women, older adults, individuals with disabilities, and athletes. Exclusions were applied to studies that (1) focused on technology or materials development (ie, technical validation), as our interest was in direct applications of technology in health and wellness, rather than preliminary stages of technological development; for instance, one research focused on preliminary technology validation without applying findings to enhance health-related activities, missing our application-focused criteria, although it mentioned motion tracking, smart systems, inertial measurement unit, and ADLs [ 129 ]; (2) presented a data set without further analysis, as our aim was to understand the implications of data on ADLs, necessitating detailed data interpretation; (3) investigated activities in clinical scenarios such as injury, impairments, hospitalization, rehabilitation, etc, because our focus was on everyday activities rather than those strictly within clinical settings; (4) were applied to nonhuman subjects to maintain the applicability of findings to human health and wellness; or (5) did not include any user study results or did not clearly explain their findings, as comprehensible and applicable user data are crucial for informing practical health care and wellness interventions ( Textbox 1 ).…”

Section: Methodsmentioning

confidence: 99%

Motion Tracking of Daily Living and Physical Activities in Health Care: Systematic Review From Designers’ Perspective

Wei,

Wang

2024

JMIR Mhealth Uhealth

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Background Motion tracking technologies serve as crucial links between physical activities and health care insights, facilitating data acquisition essential for analyzing and intervening in physical activity. Yet, systematic methodologies for evaluating motion tracking data, especially concerning user activity recognition in health care applications, remain underreported. Objective This study aims to systematically review motion tracking in daily living and physical activities, emphasizing the critical interaction among devices, users, and environments from a design perspective, and to analyze the process involved in health care application research. It intends to delineate the design and application intricacies in health care contexts, focusing on enhancing motion tracking data’s accuracy and applicability for health monitoring and intervention strategies. Methods Using a systematic review, this research scrutinized motion tracking data and their application in health care and wellness, examining studies from Scopus, Web of Science, EBSCO, and PubMed databases. The review used actor network theory and data-enabled design to understand the complex interplay between humans, devices, and environments within these applications. Results Out of 1501 initially identified studies, 54 (3.66%) were included for in-depth analysis. These articles predominantly used accelerometer and gyroscope sensors (n=43, 80%) to monitor and analyze motion, demonstrating a strong preference for these technologies in capturing both dynamic and static activities. While incorporating portable devices (n=11, 20%) and multisensor configurations (n=16, 30%), the application of sensors across the body (n=15, 28%) and within physical spaces (n=17, 31%) highlights the diverse applications of motion tracking technologies in health care research. This diversity reflects the application’s alignment with activity types ranging from daily movements to specialized scenarios. The results also reveal a diverse participant pool, including the general public, athletes, and specialized groups, with a focus on healthy individuals (n=31, 57%) and athletes (n=14, 26%). Despite this extensive application range, the focus primarily on laboratory-based studies (n=39, 72%) aimed at professional uses, such as precise activity identification and joint functionality assessment, emphasizes a significant challenge in translating findings from controlled environments to the dynamic conditions of everyday physical activities. Conclusions This study’s comprehensive investigation of motion tracking technology in health care research reveals a significant gap between the methods used for data collection and their practical application in real-world scenarios. It proposes an innovative approach that includes designers in the research process, emphasizing the importance of incorporating data-enabled design framework. This ensures that motion data collection is aligned with the dynamic and varied nature of daily living and physical activities. Such integration is crucial for developing health applications that are accessible, intuitive, and tailored to meet diverse user needs. By leveraging a multidisciplinary approach that combines design, engineering, and health sciences, the research opens new pathways for enhancing the usability and effectiveness of health technologies.

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Section: Methodsmentioning

confidence: 99%

Motion Tracking of Daily Living and Physical Activities in Health Care: Systematic Review From Designers’ Perspective

Wei,

Wang

2024

JMIR Mhealth Uhealth

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“…Ayman et al [ 38 ] recognized human activity by locating sensors on the hand, extracting multimodal data, in order to obtain healthier lifestyles. The activities carried out for the analysis were washing windows, cutting with a knife, eating, playing on a computer, and sending text messages using a keyboard and a pen.…”

Section: Background and Related Workmentioning

confidence: 99%

Design and Implementation of a Position, Speed and Orientation Fuzzy Controller Using a Motion Capture System to Operate a Wheelchair Prototype

Callejas-Cuervo

Gonźalez-Cely

Bastos

2021

Sensors

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The design and implementation of an electronic system that involves head movements to operate a prototype that can simulate future movements of a wheelchair was developed here. The controller design collects head-movements data through a MEMS sensor-based motion capture system. The research was divided into four stages: First, the instrumentation of the system using hardware and software; second, the mathematical modeling using the theory of dynamic systems; third, the automatic control of position, speed, and orientation with constant and variable speed; finally, system verification using both an electronic controller test protocol and user experience. The system involved a graphical interface for the user to interact with it by executing all the controllers in real time. Through the System Usability Scale (SUS), a score of 78 out of 100 points was obtained from the qualification of 10 users who validated the system, giving a connotation of “very good”. Users accepted the system with the recommendation to improve safety by using laser sensors instead of ultrasonic range modules to enhance obstacle detection.

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“…Common devices include accelerometers, gyroscopes, and infrared and ultrasonic sensors [ 6 ]. Among them, the inertial measurement unit (IMU) sensor is chosen due to its high sampling rate, rapid detection of inertial parameters (e.g., linear acceleration, angular acceleration, and quaternion of the limb), low power consumption, and high precision [ 7 ]. It is widely used in the field of HAR to form a body area network (BAN) [ 8 ] to identify the whole-body movement.…”

Section: Introductionmentioning

confidence: 99%

Hybrid Learning Models for IMU-Based HAR with Feature Analysis and Data Correction

Tseng,

Wen

2023

Sensors

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This paper proposes a novel approach to tackle the human activity recognition (HAR) problem. Four classes of body movement datasets, namely stand-up, sit-down, run, and walk, are applied to perform HAR. Instead of using vision-based solutions, we address the HAR challenge by implementing a real-time HAR system architecture with a wearable inertial measurement unit (IMU) sensor, which aims to achieve networked sensing and data sampling of human activity, data pre-processing and feature analysis, data generation and correction, and activity classification using hybrid learning models. Referring to the experimental results, the proposed system selects the pre-trained eXtreme Gradient Boosting (XGBoost) model and the Convolutional Variational Autoencoder (CVAE) model as the classifier and generator, respectively, with 96.03% classification accuracy.

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Smart System for Recognizing Daily Human Activities Based on Wrist IMU Sensors

Cited by 5 publications

References 17 publications

Motion Tracking of Daily Living and Physical Activities in Health Care: Systematic Review From Designers’ Perspective

Motion Tracking of Daily Living and Physical Activities in Health Care: Systematic Review From Designers’ Perspective

Design and Implementation of a Position, Speed and Orientation Fuzzy Controller Using a Motion Capture System to Operate a Wheelchair Prototype

Hybrid Learning Models for IMU-Based HAR with Feature Analysis and Data Correction

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