Cloud-Based Wheelchair Assist System for Mobility Impaired Individuals

Ma, Congcong; Li, Wenfeng; Cao, Jingjing; Gravina, Raffaele; Fortino, Giancarlo

doi:10.1007/978-3-319-45940-0_10

Cited by 10 publications

(4 citation statements)

References 25 publications

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“…Compared with the previous research on smart cushion design that use pressure sensors and accelerometers [ 26 , 28 , 33 ] as shown in Table 1 , our novel cushion shows three advantages: it is one integration unit that better combine the pressure sensors and accelerometer; it is easy to deploy, since all the sensors were deployed on the cushion seat; it is noninvasive, with the facility to monitor the sitting postures and activities. …”

Section: Measurement Methodsmentioning

confidence: 99%

“…Approximate entropy features of Center-of-Pressure (CoP) were used to reflect the irregularity of oscillations. Our former research [ 33 ] used 3 pressure sensors (two of which on the seat and the third one on the backrest) and an accelerometer attached on the waist to detect user’s postures sitting on a smart wheelchair. Different kinds of body swing movement can be recognized and experimental results showed high classification accuracy.…”

Section: Related Workmentioning

confidence: 99%

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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: Measurement Methodsmentioning

confidence: 99%

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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“…Finally, we plan to improve our BodyCloud-based Wheelchair System [ 10 , 24 , 58 ] with the work presented in this paper.…”

Section: Discussionmentioning

confidence: 99%

“…Ding et al [ 21 ] were interested in monitoring the status of the wheelchair including seat tilt, backrest recline, and seat elevation. In a previous work [ 22 ], we proposed a cloud-computing infrastructure based on the BodyCloud platform [ 23 , 24 ], and a pressure sensor cushion was used to monitor body postures of wheelchair users.…”

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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An Embedded Risk Prediction System for Wheelchair Safety Driving

et al. 2018

Internet of Things

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Cloud-Based Wheelchair Assist System for Mobility Impaired Individuals

Cited by 10 publications

References 25 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

Posture Detection Based on Smart Cushion for Wheelchair Users

An Embedded Risk Prediction System for Wheelchair Safety Driving

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