A context-aware smart seat

Benocci, Marco; Farella, Elisabetta; Benini, Luca

doi:10.1109/iwasi.2011.6004697

Cited by 18 publications

(6 citation statements)

References 6 publications

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“…In one type, the sensor was attached to the seat only [26,28,29], and in the other type the sensor was attached to both the seat and backrest [9,[30][31][32]. In addition to the pressure sensor, Benocci et al [8] used additional sensors such as kinetic related and temperature sensors to classify the sitting postures. Most of the previous studies attempted to classify the sitting upright posture in which the waist is straight, and the feet are placed flat on the floor, the postures in which the upper body is tilted forward, backward, left, or right, and the postures in which the left or right leg is crossed.…”

Section: Sitting Posture Classificationmentioning

confidence: 99%

“…To realize this system, it is necessary for the process of classifying the sitting posture accurately in real time to precede the development of the posture monitoring system. Previous studies have adopted several conventional algorithms for classifying sitting postures such as the Hidden Markov Models (HMM), Naïve Bayes (NB) classifier and k-nearest neighbor (kNN) classifier [8][9][10]. Conventional machine learning algorithms including neural network, support vector machine (SVM), and kNN are still being adopted in various research objectives such as fault diagnosis, wind speed prediction, and thermal anomalies identification [11][12][13][14].…”

Section: Introductionmentioning

confidence: 99%

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Classification of Children’s Sitting Postures Using Machine Learning Algorithms

et al. 2018

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Sitting on a chair in an awkward posture or sitting for a long period of time is a risk factor for musculoskeletal disorders. A postural habit that has been formed cannot be changed easily. It is important to form a proper postural habit from childhood as the lumbar disease during childhood caused by their improper posture is most likely to recur. Thus, there is a need for a monitoring system that classifies children's sitting postures. The purpose of this paper is to develop a system for classifying sitting postures for children using machine learning algorithms. The convolutional neural network (CNN) algorithm was used in addition to the conventional algorithms: Naïve Bayes classifier (NB), decision tree (DT), neural network (NN), multinomial logistic regression (MLR), and support vector machine (SVM). To collect data for classifying sitting postures, a sensing cushion was developed by mounting a pressure sensor mat (8 × 8) inside children's chair seat cushion. Ten children participated, and sensor data was collected by taking a static posture for the five prescribed postures. The accuracy of CNN was found to be the highest as compared with those of the other algorithms. It is expected that the comprehensive posture monitoring system would be established through future research on enhancing the classification algorithm and providing an effective feedback system.

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Section: Sitting Posture Classificationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Classification of Children’s Sitting Postures Using Machine Learning Algorithms

et al. 2018

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“…When acquired data is used to classify patterns [41,43,58,59] (this is mostly done with data coming from sensor matrices instead of a single sensor) the spatial resolution and the number of sensing points are crucial for the measurement; this is confirmed by the fact that sometimes the area of pressure carries more information than pressure magnitude itself [60]. Then this type of sensors should be used in matrices configuration when the area of pressure shape is the parameter to measure or the feature to be classified.…”

Section: Discussionmentioning

confidence: 99%

Force Sensing Resistor and Evaluation of Technology for Wearable Body Pressure Sensing

2016

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Wearable technologies are gaining momentum and widespread diffusion. Thanks to devices such as activity trackers, in form of bracelets, watches, or anklets, the end-users are becoming more and more aware of their daily activity routine, posture, and training and can modify their motor-behavior. Activity trackers are prevalently based on inertial sensors such as accelerometers and gyroscopes. Loads we bear with us and the interface pressure they put on our body also affect posture. A contact interface pressure sensing wearable would be beneficial to complement inertial activity trackers. What is precluding force sensing resistors (FSR) to be the next best seller wearable? In this paper, we provide elements to answer this question. We build an FSR based on resistive material (Velostat) and printed conductive ink electrodes on polyethylene terephthalate (PET) substrate; we test its response to pressure in the range 0–2.7 kPa. We present a state-of-the-art review, filtered by the need to identify technologies adequate for wearables. We conclude that the repeatability is the major issue yet unsolved.

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“…Hu et al [ 35 ] proposed PoSeat, a smart cushion equipped with an accelerometer and pressure sensors for chronic back pain prevention using a hybrid SVM classifier. Benocci et al [ 36 ] proposed a method using five pressure sensors and k-Nearest Neighbour (kNN) was used to classify six different postures. Bao et al [ 37 ] used a pressure cushion to recognize sitting postures by means of a density-based clustering method.…”

Section: Related Workmentioning

confidence: 99%

“…Studies have shown that a lot of different algorithms can be used for the classification of sitting postures with satisfactory accuracy ranging from 90.9% to 99.5% [ 36 , 37 , 40 , 44 ]. Since the performance of the classification results is highly dependent on the used data, we compared the five algorithms as referred to before.…”

Section: System Evaluationmentioning

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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A context-aware smart seat

Cited by 18 publications

References 6 publications

Classification of Children’s Sitting Postures Using Machine Learning Algorithms

Classification of Children’s Sitting Postures Using Machine Learning Algorithms

Force Sensing Resistor and Evaluation of Technology for Wearable Body Pressure Sensing

Posture Detection Based on Smart Cushion for Wheelchair Users

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