Sam RY, Lau YFP, Lau Y, Lau ST. Types, functions and mechanisms of robot-assisted intervention for fall prevention: A systematic scoping review.
Arch Gerontol Geriatr 2023;
115:105117. [PMID:
37422967 DOI:
10.1016/j.archger.2023.105117]
[Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/10/2023] [Revised: 06/21/2023] [Accepted: 07/03/2023] [Indexed: 07/11/2023]
Abstract
BACKGROUND
Any individual may experience accidental falls, particularly older adults. Although robots can prevent falls, knowledge of their fall-preventive use is limited.
OBJECTIVE
To explore the types, functions, and mechanisms of robot-assisted intervention for fall prevention.
METHODS
A systematic scoping review of global literature published from inception to January 2022 was conducted according to Arksey and O'Malley's five-step framework. Nine electronic databases, namely, PubMed, Embase, CINAHL, IEEE Xplore, the Cochrane Library, Scopus, Web of Science, PsycINFO, and ProQuest, were searched.
RESULTS
Seventy-one articles were found with developmental (n = 63), pilot (n = 4), survey (n = 3), and proof-of-concept (n = 1) designs across 14 countries. Six types of robot-assisted intervention were found, namely cane robots, walkers, wearables, prosthetics, exoskeletons, rollators, and other miscellaneous. Five main functions were observed including (i) detection of user fall, (ii) estimation of user state, (iii) estimation of user motion, (iv) estimation of user intentional direction, and (v) detection of user balance loss. Two categories of mechanisms of robots were found. The first category was executing initiation of incipient fall prevention such as modeling, measurement of user-robot distance, estimation of center of gravity, estimation and detection of user state, estimation of user intentional direction, and measurement of angle. The second category was achieving actualization of incipient fall prevention such as adjust optimal posture, automated braking, physical support, provision of assistive force, reposition, and control of bending angle.
CONCLUSIONS
Existing literature regarding robot-assisted intervention for fall prevention is in its infancy. Therefore, future research is required to assess its feasibility and effectiveness.
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