Method of recognizing sleep postures based on air pressure sensor and convolutional neural network: For an air spring mattress

Yao, Chao; Liu, Tao; Shen, Liming

doi:10.1016/j.engappai.2023.106009

Cited by 13 publications

(5 citation statements)

References 35 publications

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“…The muscle contribution index (calculated from the iEMG) can determine the magnitude of the degree of muscle force generation and classify the muscle contribution level. Based on sEMG and motion capture system, the muscle and joint comfort evaluation process integrate multiple biological and physiological signals ( Chao et al, 2023 ), effectively improves movement and behaviour analysis accuracy, and evaluates fatigue and comfort during furniture use.…”

Section: Discussionmentioning

confidence: 99%

Effect of different postures and loads on joint motion and muscle activity in older adults during overhead retrieval

Zhou,

Xu,

Huang

et al. 2024

Front. Physiol.

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Introduction: Pain is a common health problem among older adults worldwide. Older adults tend to suffer from arm, lumbar, and back pain when using hanging cabinets.Methods: This study used surface electromyography to record muscle activity and a motion capture system to record joint motion to research effects of different loads and retrieval postures on muscle activity and joint range of motion when older adults retrieve objects from a high place, to provide optimised feedback for the design of hanging cabinet furniture.Results: We found that: 1) The activity of BB (Biceps brachii) on the side of the body interacting with the cabinet door was greater than that of UT (Upper trapezius) and BR (Brachial radius) when retrieving objects from a high place, the activity of UT on the side of the body interacting with a heavy object was greater than that of BB and BR. 2) The activity of UT decreases when the shoulder joint angle is greater than 90°, but the activity of BB increases as the angle increases. In contrast, increasing the object’s mass causes the maximum load on the shoulder joint. 3) Among the different postures for overhead retrieval, alternating between the right and left hand is preferable for the overhead retrieval task. 4) Age had the most significant effect on overhead retrieval, followed by height (of person), and load changes were significantly different only at the experiment’s left elbow joint and the L.BR. 5) Older adults took longer and exerted more effort to complete the task than younger adults, and static exercise in older adults may be more demanding on muscle activity in old age than powered exercise.Conclusion: These results help to optimise the design of hanging cabinet furniture. Regarding the height of hanging cabinets, 180 cm or less is required for regular retrieval movements if the human height is less than 150 cm. Concerning the depth of the hanging cabinets, different heights chose different comfort distances, which translated into the depth of the hanging cabinets; the greater the height, the greater the depth of the hanging cabinets to use.

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Section: Discussionmentioning

confidence: 99%

Effect of different postures and loads on joint motion and muscle activity in older adults during overhead retrieval

Zhou,

Xu,

Huang

et al. 2024

Front. Physiol.

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show abstract

“…The current sleep posture recognition and adjustment system is realized by different hardware and sensors, which not only increases the number of sensors and production cost, but also increases the complexity of system design and the difficulty of data computation. The sleep posture recognition model for air beds proposed in 13 still uses image data as input, which is computationally intensive and not very portable. In contrast, the design of air springs 14 , as well as the design of the air bed structure 15 makes the integration of sleep posture recognition and mattress adaptive adjustment system possible.…”

Section: Related Workmentioning

confidence: 99%

A dual fusion recognition model for sleep posture based on air mattress pressure detection

Li,

Zhou,

Zhou

2024

Sci Rep

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In order to solve the difficult portability problem of traditional non-invasive sleeping posture recognition algorithms arising from the production cost and computational cost, this paper proposes a sleeping posture recognition model focusing on human body structural feature extraction and integration of feature space and algorithms based on a specific air-spring mattress structure, called SPR-DE (SPR-DE is the Sleep Posture Recognition-Data Ensemble acronym form). The model combines SMR (SMR stands for Principle of Spearman Maximal Relevance) with horizontal and vertical division based on the barometric pressure signals in the human body’s backbone region to reconstruct the raw pressure data into strongly correlated non-image features of the sleep postures in different parts and directions and construct the feature set. Finally, the recognit-ion of the two sleep postures is accomplished using the AdaBoost-SVM integrated classifier. SPR-DE is compared with the base and integrated classifiers to verify its performance. The experimental results show that the amount of significant features helps the algorithm to classify different sleeping patterns more accurately, and the f1 score of the SPR-DE model determined by the comparison experiments is 0.998, and the accuracy can reach 99.9%. Compared with other models, the accuracy is improved by 2.9% ~ 7.7%, and the f1-score is improved by 0.029 ~ 0.076. Therefore, it is concluded that the SMR feature extraction strategy in the SPR-DE model and the AdaBoost-SVM can achieve high accuracy and strong robustness in the task of sleep posture recognition in a small area, low-density air-pressure mattress, taking into account the comfort of the mattress structural design and the sleep posture recognition, integrated with the mattress adaptive adjustment system.

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“…In addition, some non-wearable devices can be also applied for sleep posture detection. The smart mattress can be used to recognize the sleep posture with the pressure signals measured by the pressure sensors [ [16] , [17] , [18] , [19] , [20] ]. For illustrating the pressure map, more pressure sensors are essential, resulting in the high cost.…”

Section: Introductionmentioning

confidence: 99%

A smart detection method for sleep posture based on a flexible sleep monitoring belt and vital sign signals

He,

Fang,

Liu

et al. 2024

Heliyon

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Method of recognizing sleep postures based on air pressure sensor and convolutional neural network: For an air spring mattress

Cited by 13 publications

References 35 publications

Effect of different postures and loads on joint motion and muscle activity in older adults during overhead retrieval

Effect of different postures and loads on joint motion and muscle activity in older adults during overhead retrieval

A dual fusion recognition model for sleep posture based on air mattress pressure detection

A smart detection method for sleep posture based on a flexible sleep monitoring belt and vital sign signals

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