Human Fall Detection Improvement Based on Artificial Neural Network and Optimized Zero Moment Point Algorithms

Tan, Xiaojing; Jin, Weiliang; Geng, Xiaodong; Wejinya, Uchechukwu C.; Shi, Guangyi; Yan, Wei

doi:10.1109/rcar.2018.8621671

Cited by 1 publication

(1 citation statement)

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“…Fusing information from different types of sensors might improve the accuracy, e.g. besides IMUs on the belt, Tan et al [22] achieved an accuracy of over 98% for fall detection by integrating data from pressure sensors under the feet.…”

Section: Introductionmentioning

confidence: 99%

Comfort Wearables for In-Flight Sitting Posture Recognition

Yao,

Yang,

Vledder

et al. 2023

IEEE Access

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Wearables are used to recognize human activities in various applications. However, there is limited evidence on the comfort feelings in using wearables, which is crucial for the adoption and longterm engagement of users in those applications. In this paper, we propose the concept of comfort wearables in the context of in-flight posture recognition. A comfort wearable and a tight-fit version, using identical hardware and software architecture, were prototyped and tested by 35 participants in a Boeing 737 cabin. During the usage of each wearable, participants were asked to perform seven frequently observed in-flight sitting postures and report their overall comfort/discomfort afterwards. A multilayer perceptron neural network was used to classify those activities. Experiment results indicated that participants appreciated the comfort wearable, rating it with significantly higher comfort scores and lower discomfort scores. Crossvalidation results also revealed that using the comfort wearable achieved even better accuracy (74.8%) than using the tight-fit wearable (65.8%) in posture recognition. Outcomes of the study demonstrate that ergonomic design and technical accuracy are not competing factors in the wearable design and highlight the opportunities for designing and using comfort wearables in broader contexts.

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Section: Introductionmentioning

confidence: 99%