Journal of Siberian Federal University. Humanities & Social Sciences / Intelligent Weight Machines with Load Adjustment Based on Servo Drive (Project Under Development)

Full text (.pdf)
Issue
Journal of Siberian Federal University. Humanities & Social Sciences. 2021 14 (2)
Authors
Epishev, Vitaliy V.; Maksakov, Dmitriy G.; Erlikh, Vadim V.; Petrov, Alexey A.; Petrova, Lina N.
Contact information
Epishev, Vitaliy V.: South Ural State University (National Research University) Chelyabinsk, Russian Federation; ; ORCID: 0000-0002-7284-7388; Maksakov, Dmitriy G.: Dzhimnes company Chelyabinsk, Russian Federation; Erlikh, Vadim V.: South Ural State University (National Research University) Chelyabinsk, Russian Federation; Petrov, Alexey A.: South Ural State University (National Research University) Chelyabinsk, Russian Federation; Petrova, Lina N.: South Ural State University (National Research University) Chelyabinsk, Russian Federation
Keywords
weight machine; servo drive; ECG monitoring; control of the center of pressure; intelligent training machine
Abstract

The study aims to create a new generation weight machine for obtaining objective information (digital control) about the performed load and the response of the cardiovascular system and the musculoskeletal system during exercise. Patent search, publication analysis, marketing research, and three research and development activities were carried out. Technical requirements for intelligent weight machines were formulated. Based on the Gravitron training machine, 2 prototypes were created, where traditional weight stacks were replaced by a system consisting of a servo drive and control system components. To control the biomechanics of movement, a prototype consisting of a force platform integrated into the seat of the weight machine was developed. Biofeedback is implemented by displaying data on the center of pressure in the initial position and during exercise. A prototype of an ECG monitoring system in standard lead I was created, which is software and hardware built into the bar of a weight machine. The primary results demonstrate the technical feasibility of creating a new class of training machines with biofeedback and continuous monitoring of the user’s state. Combining several intelligent machines into a single gym ecosystem will allow developing fundamentally new approaches to the training process

Pages
241–249
DOI
10.17516/1997-1370-0715
Paper at repository of SibFU
https://elib.sfu-kras.ru/handle/2311/137988

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