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Citation |
Pulido Herrera E, Ruiz Olaya AF, Sierra Bueno DA. Proc. Inst. Mech. Eng. Pt. H J. Eng. Med. 2023; 237(3): 327-335. |
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Copyright |
(Copyright © 2023, Institution of Mechanical Engineers, Publisher SAGE Publishing) |
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DOI |
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PMID |
36974031 |
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Abstract |
An emerging source of information to recognize individuals' characteristics are the walking pattern-related parameters. The elderly can be one of the populations that can benefit most from recognition-based applications, which may help to increase their possibilities of living independently at home. Approaches have been mostly focused on gait events' identification or assessment; nonetheless, such information can also be used to obtain seniors' characteristics that depend on physiological or environmental factors. These factors can be useful to provide a customized assistance based on contextual information. In this paper, we propose a method focused on seniors, to detect steps, and to recognize gender and type of shoes by using only the initial foot contact (IC) data obtained from inertial sensors during semi-controlled walking. Data were collected from 20 older adults who walked at self-speed in a natural environment. The method consists of first clustering the IC using k-means; then, a trained recurrent neural network recognizes gender, type of shoes, and the step phases (IC and other phases); to finally conduct step detection (SD) using a ruled-based method. The method recognizes gender and the type of shoes with an accuracy of 93% and 83.07%, respectively, whereas there were not misrecognitions of the step phases. SD achieved a mean absolute percentage error equal to 0.64%. The good results show that the method is appropriate for users' characteristics recognition applications without depending on assumptions based on individualities. Likewise, the method can be useful to monitor physical activity or systems aimed to keep safe older adults. Language: en |
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Keywords |
Aged; Humans; Foot; inertial sensors; *Gait/physiology; *Shoes; elderly monitoring; Elderly step detection; gender recognition; recurrent neural networks; Walking/physiology |