A Fatigue Detect System Based on Activity Recognition

Ma, Congcong; Li, Wenfeng; Cao, Jingjing; Wang, Shuwu; Wu, Lei

doi:10.1007/978-3-319-11692-1_26

Cited by 14 publications

(6 citation statements)

References 12 publications

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“…The information hidden behind the posture/activity is also very meaningful to reflect the user’s health status. As pressure sensor based smart cushion is unobtrusive, several related works adopted it to recognize physical activity [ 10 ], to measure comfort and wellness [ 11 ], posture and fatigue of users sitting on normal chairs and wheelchairs [ 12 , 13 ]. In the following paragraph, we introduce the related work on activity level assessment, sitting posture analyze and cushion based systems.…”

Section: Related Workmentioning

confidence: 99%

Activity Level Assessment Using a Smart Cushion for People with a Sedentary Lifestyle

Gravina

et al. 2017

Sensors

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As a sedentary lifestyle leads to numerous health problems, it is important to keep constant motivation for a more active lifestyle. A large majority of the worldwide population, such as office workers, long journey vehicle drivers and wheelchair users, spends several hours every day in sedentary activities. The postures that sedentary lifestyle users assume during daily activities hide valuable information that can reveal their wellness and general health condition. Aiming at mining such underlying information, we developed a cushion-based system to assess their activity levels and recognize the activity from the information hidden in sitting postures. By placing the smart cushion on the chair, we can monitor users’ postures and body swings, using the sensors deployed in the cushion. Specifically, we construct a body posture analysis model to recognize sitting behaviors. In addition, we provided a smart cushion that effectively combine pressure and inertial sensors. Finally, we propose a method to assess the activity levels based on the evaluation of the activity assessment index (AAI) in time sliding windows. Activity level assessment can be used to provide statistical results in a defined period and deliver recommendation exercise to the users. For practical implications and actual significance of results, we selected wheelchair users among the participants to our experiments. Features in terms of standard deviation and approximate entropy were compared to recognize the activities and activity levels. The results showed that, using the novel designed smart cushion and the standard deviation features, we are able to achieve an accuracy of (>89%) for activity recognition and (>98%) for activity level recognition.

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Section: Related Workmentioning

confidence: 99%

Activity Level Assessment Using a Smart Cushion for People with a Sedentary Lifestyle

Gravina

et al. 2017

Sensors

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“…Berolo et al reported that mobile hand-held device users complain of discomfort in at least one area of the upper extremities, upper back, or neck 4 ) . Long-term use of smartphones causes continuous mechanical stress on the tendons, muscles, and perimetric tissue, which can induce musculoskeletal symptoms of visual display terminal (VDT) syndrome 5 , 6 ) . Work with VDTs has been reported to cause pain in the neck and shoulders due to increased tension caused by a continuously flexed neck posture 7 ) .…”

Section: Introductionmentioning

confidence: 99%

The effects of smartphone use on upper extremity muscle activity and pain threshold

et al. 2015

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[Purpose] The purpose of this study was to determine whether muscle activity and pressure-induced pain in the upper extremities are affected by smartphone use, and to compare the effects of phone handling with one hand and with both hands. [Subjects] The study subjects were asymptomatic women 20–22 years of age. [Methods] The subjects sat in a chair with their feet on the floor and the elbow flexed, holding a smartphone positioned on the thigh. Subsequently, the subjects typed the Korean anthem for 3 min, one-handed or with both hands. Each subject repeated the task three times, with a 5-min rest period between tasks to minimize fatigue. Electromyography (EMG) was used to record the muscle activity of the upper trapezius (UT), extensor pollicis longus (EPL), and abductor pollicis (AP) during phone operation. We also used a dolorimeter to measure the pressure-induced pain threshold in the UT. [Results] We observed higher muscle activity in the UT, AP, and EPL in one-handed smartphone use than in its two-handed use. The pressure-induced pain threshold of the UT was lower after use of the smartphone, especially after one-handed use. [Conclusion] Our results show that smartphone operation with one hand caused greater UT pain and induced increased upper extremity muscle activity.

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“…Due to its unobtrusiveness, several literature works adopted a pressure sensor-based cushion to recognize physical activity, posture and fatigue of individuals sitting on standard chairs or on wheelchairs [ 25 ]. Specifically, there are two main production technologies of smart cushions for pressure detection: the first one is based on a pressure sensor array, whereas the second one relies on the use of fewer individual sensors deployed on the seat and backrest.…”

Section: Related Workmentioning

confidence: 99%

Posture Detection Based on Smart Cushion for Wheelchair Users

Gravina

et al. 2017

Sensors

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The postures of wheelchair users can reveal their sitting habit, mood, and even predict health risks such as pressure ulcers or lower back pain. Mining the hidden information of the postures can reveal their wellness and general health conditions. In this paper, a cushion-based posture recognition system is used to process pressure sensor signals for the detection of user’s posture in the wheelchair. The proposed posture detection method is composed of three main steps: data level classification for posture detection, backward selection of sensor configuration, and recognition results compared with previous literature. Five supervised classification techniques—Decision Tree (J48), Support Vector Machines (SVM), Multilayer Perceptron (MLP), Naive Bayes, and k-Nearest Neighbor (k-NN)—are compared in terms of classification accuracy, precision, recall, and F-measure. Results indicate that the J48 classifier provides the highest accuracy compared to other techniques. The backward selection method was used to determine the best sensor deployment configuration of the wheelchair. Several kinds of pressure sensor deployments are compared and our new method of deployment is shown to better detect postures of the wheelchair users. Performance analysis also took into account the Body Mass Index (BMI), useful for evaluating the robustness of the method across individual physical differences. Results show that our proposed sensor deployment is effective, achieving 99.47% posture recognition accuracy. Our proposed method is very competitive for posture recognition and robust in comparison with other former research. Accurate posture detection represents a fundamental basic block to develop several applications, including fatigue estimation and activity level assessment.

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A Fatigue Detect System Based on Activity Recognition

Cited by 14 publications

References 12 publications

Activity Level Assessment Using a Smart Cushion for People with a Sedentary Lifestyle

Activity Level Assessment Using a Smart Cushion for People with a Sedentary Lifestyle

The effects of smartphone use on upper extremity muscle activity and pain threshold

Posture Detection Based on Smart Cushion for Wheelchair Users

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