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A Systematic Review of Wearable Sensor-Based Technologies for Fall Risk Assessment in Older Adults.
Chen, Manting; Wang, Hailiang; Yu, Lisha; Yeung, Eric Hiu Kwong; Luo, Jiajia; Tsui, Kwok-Leung; Zhao, Yang.
Afiliação
  • Chen M; School of Public Health (Shenzhen), Sun Yat-sen University, Shenzhen 518000, China.
  • Wang H; School of Design, The Hong Kong Polytechnic University, Hung Hom, Hong Kong, China.
  • Yu L; Shenzhen Enstech Technology Co., Ltd., Shenzhen 518000, China.
  • Yeung EHK; Department of Physiotherapy, The University of Hong Kong-Shenzhen Hospital, Shenzhen 518000, China.
  • Luo J; School of Public Health (Shenzhen), Sun Yat-sen University, Shenzhen 518000, China.
  • Tsui KL; Grado Department of Industrial and Systems Engineering, Virginia Polytechnic Institute and State University, Blacksburg, VA 24061, USA.
  • Zhao Y; School of Public Health (Shenzhen), Sun Yat-sen University, Shenzhen 518000, China.
Sensors (Basel) ; 22(18)2022 Sep 07.
Article em En | MEDLINE | ID: mdl-36146103
Falls have been recognized as the major cause of accidental death and injury in people aged 65 and above. The timely prediction of fall risks can help identify older adults prone to falls and implement preventive interventions. Recent advancements in wearable sensor-based technologies and big data analysis have spurred the development of accurate, affordable, and easy-to-use approaches to fall risk assessment. The objective of this study was to systematically assess the current state of wearable sensor-based technologies for fall risk assessment among community-dwelling older adults. Twenty-five of 614 identified research articles were included in this review. A comprehensive comparison was conducted to evaluate these approaches from several perspectives. In general, these approaches provide an accurate and effective surrogate for fall risk assessment. The accuracy of fall risk prediction can be influenced by various factors such as sensor location, sensor type, features utilized, and data processing and modeling techniques. Features constructed from the raw signals are essential for predictive model development. However, more investigations are needed to identify distinct, clinically interpretable features and develop a general framework for fall risk assessment based on the integration of sensor technologies and data modeling.
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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Vida Independente / Dispositivos Eletrônicos Vestíveis Tipo de estudo: Etiology_studies / Prognostic_studies / Risk_factors_studies / Systematic_reviews Limite: Aged / Humans Idioma: En Ano de publicação: 2022 Tipo de documento: Article

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Vida Independente / Dispositivos Eletrônicos Vestíveis Tipo de estudo: Etiology_studies / Prognostic_studies / Risk_factors_studies / Systematic_reviews Limite: Aged / Humans Idioma: En Ano de publicação: 2022 Tipo de documento: Article