Wearable Sensor Systems for Fall Risk Assessment: A Review

被引:45
作者
Subramaniam, Sophini [1 ]
Faisal, Abu Ilius [2 ]
Deen, M. Jamal [1 ,2 ]
机构
[1] McMaster Univ, Sch Biomed Engn, Hamilton, ON, Canada
[2] McMaster Univ, Elect & Comp Engn, Hamilton, ON, Canada
来源
FRONTIERS IN DIGITAL HEALTH | 2022年 / 4卷
基金
加拿大自然科学与工程研究理事会;
关键词
fall risk assessment; fall detection; wearables; smart insole; inertial sensors; plantar pressure; gait analysis; machine learning; BERG BALANCE SCALE; HEALTH-CARE; GAIT; STABILITY; PARAMETERS; ALGORITHM; NETWORK; WALKING; PEOPLE;
D O I
10.3389/fdgth.2022.921506
中图分类号
R19 [保健组织与事业(卫生事业管理)];
学科分类号
摘要
Fall risk assessment and fall detection are crucial for the prevention of adverse and long-term health outcomes. Wearable sensor systems have been used to assess fall risk and detect falls while providing additional meaningful information regarding gait characteristics. Commonly used wearable systems for this purpose are inertial measurement units (IMUs), which acquire data from accelerometers and gyroscopes. IMUs can be placed at various locations on the body to acquire motion data that can be further analyzed and interpreted. Insole-based devices are wearable systems that were also developed for fall risk assessment and fall detection. Insole-based systems are placed beneath the sole of the foot and typically obtain plantar pressure distribution data. Fall-related parameters have been investigated using inertial sensor-based and insole-based devices include, but are not limited to, center of pressure trajectory, postural stability, plantar pressure distribution and gait characteristics such as cadence, step length, single/double support ratio and stance/swing phase duration. The acquired data from inertial and insole-based systems can undergo various analysis techniques to provide meaningful information regarding an individual's fall risk or fall status. By assessing the merits and limitations of existing systems, future wearable sensors can be improved to allow for more accurate and convenient fall risk assessment. This article reviews inertial sensor-based and insole-based wearable devices that were developed for applications related to falls. This review identifies key points including spatiotemporal parameters, biomechanical gait parameters, physical activities and data analysis methods pertaining to recently developed systems, current challenges, and future perspectives.
引用
收藏
页数:20
相关论文
共 50 条
  • [31] Objective fall risk detection in stroke survivors using wearable sensor technology: a feasibility study
    Taylor-Piliae, Ruth E.
    Mohler, M. Jane
    Najafi, Bijan
    Coull, Bruce M.
    TOPICS IN STROKE REHABILITATION, 2016, 23 (06) : 393 - 399
  • [32] Sensor Technologies for Fall Detection Systems: A Review
    Singh, Anuradha
    Rehman, Saeed Ur
    Yongchareon, Sira
    Chong, Peter Han Joo
    IEEE SENSORS JOURNAL, 2020, 20 (13) : 6889 - 6919
  • [33] Assessment of Balance Instability by Wearable Sensor Systems During Postural Transitions
    Hessfeld, Vincent
    Schulleri, Katrin H.
    Lee, Dongheui
    2021 43RD ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE & BIOLOGY SOCIETY (EMBC), 2021, : 7455 - 7459
  • [34] AI-assisted assessment of fall risk in multiple sclerosis: A systematic literature review
    Mehrlatifan, Somayeh
    Molla, Razieh Yousefian
    MULTIPLE SCLEROSIS AND RELATED DISORDERS, 2024, 92
  • [35] A Novel Heuristic Fall-Detection Algorithm Based on Double Thresholding, Fuzzy Logic, and Wearable Motion Sensor Data
    Barshan, Billur
    Turan, Mustafa Sahin
    IEEE INTERNET OF THINGS JOURNAL, 2023, 10 (20) : 17797 - 17812
  • [36] Quantitative Assessment of Balance Impairment for Fall-Risk Estimation Using Wearable Triaxial Accelerometer
    Shahzad, Ahsan
    Ko, Seunguk
    Lee, Samgyu
    Lee, Jeong-A
    Kim, Kiseon
    IEEE SENSORS JOURNAL, 2017, 17 (20) : 6743 - 6751
  • [37] Instrumental Assessment of Stair Ascent in People With Multiple Sclerosis, Stroke, and Parkinson's Disease: A Wearable-Sensor-Based Approach
    Carpinella, Ilaria
    Gervasoni, Elisa
    Anastasi, Denise
    Lencioni, Tiziana
    Cattaneo, Davide
    Ferrarin, Maurizio
    IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING, 2018, 26 (12) : 2324 - 2332
  • [38] FRADE: Pervasive Platform for Fall Risk Assessment, Prevention and Fall Detection
    Silva, Joana
    Cardoso, Nuno
    Ribeiro, Jorge
    Carvalho, Alberto
    Pereira, Mariana
    Ricaldoni, Fernando
    Resende, Carlos
    Oliveira, Joao
    BIOSIGNALS: PROCEEDINGS OF THE 14TH INTERNATIONAL JOINT CONFERENCE ON BIOMEDICAL ENGINEERING SYSTEMS AND TECHNOLOGIES - VOL 4: BIOSIGNALS, 2021, : 320 - 326
  • [39] Wearable sensor networks supported by mobile devices for fall detection
    Freitas, Ricardo
    Terroso, Miguel
    Marques, Marco
    Gabriel, Joaquim
    Marques, Antonio Torres
    Simoes, Ricardo
    2014 IEEE SENSORS, 2014, : 2246 - 2249
  • [40] A Novel Embedded Deep Learning Wearable Sensor for Fall Detection
    Campanella, Sara
    Alnasef, Alaa
    Falaschetti, Laura
    Belli, Alberto
    Pierleoni, Paola
    Palma, Lorenzo
    IEEE SENSORS JOURNAL, 2024, 24 (09) : 15219 - 15229