IntelliChair: An Approach for Activity Detection and Prediction via Posture Analysis

Fu, Teng; MacLeod, A. M.

doi:10.1109/ie.2014.39

Cited by 24 publications

(13 citation statements)

References 6 publications

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“…Results showed that it is possible to detect not only different postures, but also subtle hand and head related actions like typing and nodding. Fu et al [ 30 ] proposed a robust, low-cost, sensor based system that is capable of recognizing sitting postures and activities. Eight force sensing resistors (FSRs) were placed on chair backrest and seat, and a Hidden Markov Model approach was used to establish the activity model from sitting posture sequences.…”

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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“…Pressure sensors are extensively used in [3], [4] for identifying the sedentary patterns. In [5] the pressure sensors are used for predicting the stress level of a user and in [6] for predicting the subsequent sitting activity. A smart chair is also applied to measuring the vital health signs like the heart rate through the Ballistocardiograhy (BCG) technique, Junnila et al used EMFi-film sensor installed on the seat of the chair [7].…”

Section: Introductionmentioning

confidence: 99%

Understanding Cyber Athletes Behaviour Through a Smart Chair: CS:GO and Monolith Team Scenario

Smerdov

Kiskun

Shaniiazov

et al. 2019

2019 IEEE 5th World Forum on Internet of Things (WF-IoT)

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eSports is the rapidly developing multidisciplinary domain. However, research and experimentation in eSports are in the infancy. In this work, we propose a smart chair platform -an unobtrusive approach to the collection of data on the eSports athletes and data further processing with machine learning methods. The use case scenario involves three groups of players: cyber athletes (Monolith team), semi-professional players and newbies all playing CS:GO discipline. In particular, we collect data from the accelerometer and gyroscope integrated in the chair and apply machine learning algorithms for the data analysis. Our results demonstrate that the professional athletes can be identified by their behaviour on the chair while playing the game.

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“…Sixteen pressure sensors were placed on the chair, half on the seat and the other half on the backrest. Fu et al [ 42 ] proposed a robust, low-cost, sensor based system that is capable of recognizing sitting postures and activities. Eight force sensing resistors (FSRs) were placed on chair backrests and seats, and a Hidden Markov Model approach was used to establish the activity model from sitting posture sequences.…”

Section: Related Workmentioning

confidence: 99%

Posture Detection Based on Smart Cushion for Wheelchair Users

Gravina

et al. 2017

Sensors

113

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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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IntelliChair: An Approach for Activity Detection and Prediction via Posture Analysis

Cited by 24 publications

References 6 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

Understanding Cyber Athletes Behaviour Through a Smart Chair: CS:GO and Monolith Team Scenario

Posture Detection Based on Smart Cushion for Wheelchair Users

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