The Experts below are selected from a list of 360 Experts worldwide ranked by ideXlab platform
Chiatai Chan - One of the best experts on this subject based on the ideXlab platform.
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impact of sampling rate on wearable based Fall Detection systems based on machine learning models
IEEE Sensors Journal, 2018Co-Authors: Chiayeh Hsieh, Chiatai ChanAbstract:Falls are a leading health risk for the elderly. Various wearable-based Fall Detection systems based on machine learning models have been developed to provide emergency alarms and services to improve safety and health-related quality of life. To support long-term healthcare services, the appropriate sampling rate, which plays a vital role in a Fall Detection system, must be investigated to guarantee the performance accuracy and energy efficiency. Intuitively, decreasing the sampling rate of sensor nodes can provide computing and energy savings. However, there is a lack of research exploring the performance accuracy of Fall Detection systems in terms of sampling rate, especially for machine learning models. In this paper, the effects of decreasing sampling rates (ranging from 200/128 to 3 Hz) on wearable-based Fall Detection systems are investigated based on four machine learning models: support vector machine (SVM), $k$ -nearest neighbor, Naive Bayes, and decision tree. Two emulated Fall data sets, the SisFall public data set and the proposed data set of this paper, are used to allow an objective investigation of sampling rates. The findings show that Fall Detection systems based on SVM modeling and a radial basis function could achieve at least 98% and 97% accuracy, with sampling rates of 11.6 and 5.8 Hz, respectively. Overall, the experimental results demonstrate that a sampling rate of 22 Hz is sufficient for most machine learning models to support wearable-based Fall Detection systems (accuracy ≥97%).
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novel hierarchical Fall Detection algorithm using a multiphase Fall model
Sensors, 2017Co-Authors: Chiayeh Hsieh, Kaichun Liu, Chihning Huang, Woeichyn Chu, Chiatai ChanAbstract:Falls are the primary cause of accidents for the elderly in the living environment. Reducing hazards in the living environment and performing exercises for training balance and muscles are the common strategies for Fall prevention. However, Falls cannot be avoided completely; Fall Detection provides an alarm that can decrease injuries or death caused by the lack of rescue. The automatic Fall Detection system has opportunities to provide real-time emergency alarms for improving the safety and quality of home healthcare services. Two common technical challenges are also tackled in order to provide a reliable Fall Detection algorithm, including variability and ambiguity. We propose a novel hierarchical Fall Detection algorithm involving threshold-based and knowledge-based approaches to detect a Fall event. The threshold-based approach efficiently supports the Detection and identification of Fall events from continuous sensor data. A multiphase Fall model is utilized, including free Fall, impact, and rest phases for the knowledge-based approach, which identifies Fall events and has the potential to deal with the aforementioned technical challenges of a Fall Detection system. Seven kinds of Falls and seven types of daily activities arranged in an experiment are used to explore the performance of the proposed Fall Detection algorithm. The overall performances of the sensitivity, specificity, precision, and accuracy using a knowledge-based algorithm are 99.79%, 98.74%, 99.05% and 99.33%, respectively. The results show that the proposed novel hierarchical Fall Detection algorithm can cope with the variability and ambiguity of the technical challenges and fulfill the reliability, adaptability, and flexibility requirements of an automatic Fall Detection system with respect to the individual differences.
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a zigbee based location aware Fall Detection system for improving elderly telecare
International Journal of Environmental Research and Public Health, 2014Co-Authors: Chihning Huang, Chiatai ChanAbstract:Falls are the primary cause of accidents among the elderly and frequently cause fatal and non-fatal injuries associated with a large amount of medical costs. Fall Detection using wearable wireless sensor nodes has the potential of improving elderly telecare. This investigation proposes a ZigBee-based location-aware Fall Detection system for elderly telecare that provides an unobstructed communication between the elderly and caregivers when Falls happen. The system is based on ZigBee-based sensor networks, and the sensor node consists of a motherboard with a tri-axial accelerometer and a ZigBee module. A wireless sensor node worn on the waist continuously detects Fall events and starts an indoor positioning engine as soon as a Fall happens. In the Fall Detection scheme, this study proposes a three-phase threshold-based Fall Detection algorithm to detect critical and normal Falls. The Fall alarm can be canceled by pressing and holding the emergency Fall button only when a normal Fall is detected. On the other hand, there are three phases in the indoor positioning engine: path loss survey phase, Received Signal Strength Indicator (RSSI) collection phase and location calculation phase. Finally, the location of the Faller will be calculated by a k-nearest neighbor algorithm with weighted RSSI. The experimental results demonstrate that the Fall Detection algorithm achieves 95.63% sensitivity, 73.5% specificity, 88.62% accuracy and 88.6% precision. Furthermore, the average error distance for indoor positioning is 1.15 ± 0.54 m. The proposed system successfully delivers critical information to remote telecare providers who can then immediately help a Fallen person.
Dong Xuan - One of the best experts on this subject based on the ideXlab platform.
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mobile phone based pervasive Fall Detection
Ubiquitous Computing, 2010Co-Authors: Jiangpeng Dai, Xiaole Bai, Zhimin Yang, Zhaohui Shen, Dong XuanAbstract:Falls are a major health risk that diminishes the quality of life among the elderly people. The importance of Fall Detection increases as the elderly population surges, especially with aging "baby boomers". However, existing commercial products and academic solutions all Fall short of pervasive Fall Detection. In this paper, we propose utilizing mobile phones as a platform for developing pervasive Fall Detection system. To our knowledge, we are the first to do so. We propose PerFallD, a pervasive Fall Detection system tailored for mobile phones. We design two different Detection algorithms based on the mobile phone platforms for scenarios with and without simple accessories. We implement a prototype system on the Android G1 phone and conduct extensive experiments to evaluate our system. In particular, we compare PerFallD's performance with that of existing work and a commercial product. The experimental results show that PerFallD achieves superior Detection performance and power efficiency.
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perFalld a pervasive Fall Detection system using mobile phones
IEEE International Conference on Pervasive Computing and Communications, 2010Co-Authors: Jiangpeng Dai, Xiaole Bai, Zhimin Yang, Zhaohui Shen, Dong XuanAbstract:Falls are a major health risk that diminish the quality of life among elderly people. With the elderly population surging, especially with aging “baby boomers”, Fall Detection becomes increasingly important. However, existing commercial products and academic solutions struggle to achieve pervasive Fall Detection. In this paper, we propose utilizing mobile phones as a platform for pervasive Fall Detection system development. To our knowledge, we are the first to do so. We design a Detection algorithm based on mobile phone platforms. We propose PerFallD, a pervasive Fall Detection system implemented on mobile phones. We implement a prototype system on the Android G1 phone and conduct experiments to evaluate our system. In particular, we compare PerFallD's performance with that of existing work and a commercial product. Experimental results show that PerFallD achieves strong Detection performance and power efficiency.
Mihail Popescu - One of the best experts on this subject based on the ideXlab platform.
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efficient source separation algorithms for acoustic Fall Detection using a microsoft kinect
IEEE Transactions on Biomedical Engineering, 2014Co-Authors: Mihail PopescuAbstract:Falls have become a common health problem among older adults. In previous study, we proposed an acoustic Fall Detection system (acoustic FADE) that employed a microphone array and beamforming to provide automatic Fall Detection. However, the previous acoustic FADE had difficulties in detecting the Fall signal in environments where interference comes from the Fall direction, the number of interferences exceeds FADE's ability to handle or a Fall is occluded. To address these issues, in this paper, we propose two blind source separation (BSS) methods for extracting the Fall signal out of the interferences to improve the Fall classification task. We first propose the single-channel BSS by using nonnegative matrix factorization (NMF) to automatically decompose the mixture into a linear combination of several basis components. Based on the distinct patterns of the bases of Falls, we identify them efficiently and then construct the interference free Fall signal. Next, we extend the single-channel BSS to the multichannel case through a joint NMF over all channels followed by a delay-and-sum beamformer for additional ambient noise reduction. In our experiments, we used the Microsoft Kinect to collect the acoustic data in real-home environments. The results show that in environments with high interference and background noise levels, the Fall Detection performance is significantly improved using the proposed BSS approaches.
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doppler radar sensor positioning in a Fall Detection system
International Conference of the IEEE Engineering in Medicine and Biology Society, 2012Co-Authors: Mihail Popescu, K C Ho, Marjorie Skubic, Marilyn RantzAbstract:Falling is a common health problem for more than a third of the United States population over 65. We are currently developing a Doppler radar based Fall Detection system that already has showed promising results. In this paper, we study the sensor positioning in the environment with respect to the subject. We investigate three sensor positions, floor, wall and ceiling of the room, in two experimental configurations. Within each system configuration, subjects performed Falls towards or across the radar sensors. We collected 90 Falls and 341 non Falls for the first configuration and 126 Falls and 817 non Falls for the second one. Radar signature classification was performed using a SVM classifier. Fall Detection performance was evaluated using the area under the ROC curves (AUCs) for each sensor deployment. We found that a Fall is more likely to be detected if the subject is Falling toward or away from the sensor and a ceiling Doppler radar is more reliable for Fall Detection than a wall mounted one.
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acoustic Fall Detection using a circular microphone array
International Conference of the IEEE Engineering in Medicine and Biology Society, 2010Co-Authors: Yun Li, Mihail Popescu, Zhiling Zeng, K C HoAbstract:Falling is a common health problem for elderly. It is reported that more than one third of adults 65 and older Fall each year in the United States. To address the problem, we are currently developing an acoustic Fall Detection system, FADE, which automatically detects a Fall and reports it to the caregiver. In a previous version, FADE used a 3-microphone linear array to eliminate the false alarms produced by sounds produced well above the floor level. To improve the Fall Detection in noisy and reverberant environments, we replaced the linear array by an 8-microphone circular array that can provide a better 3-D estimation of the sound location. Preliminary experiments show that the sound location estimation performed by the circular array is reliable and robust to interference. We obtained encouraging classification results on a pilot dataset with 55 Falls and 120 non-Fall sounds.
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acoustic Fall Detection using a circular microphone array
International Conference of the IEEE Engineering in Medicine and Biology Society, 2010Co-Authors: Zhiling Zeng, Mihail PopescuAbstract:Falling is a common health problem for elderly. It is reported that more than one third of adults 65 and older Fall each year in the United States. To address the problem, we are currently developing an acoustic Fall Detection system, FADE, which automatically detects a Fall and reports it to the caregiver. In a previous version, FADE used a 3-microphone linear array to eliminate the false alarms produced by sounds produced well above the floor level. To improve the Fall Detection in noisy and reverberant environments, we replaced the linear array by an 8-microphone circular array that can provide a better 3-D estimation of the sound location. Preliminary experiments show that the sound location estimation performed by the circular array is reliable and robust to interference. We obtained encouraging classification results on a pilot dataset with 55 Falls and 120 non-Fall sounds.
Theo Lynn - One of the best experts on this subject based on the ideXlab platform.
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accelerometer based human Fall Detection using convolutional neural networks
Sensors, 2019Co-Authors: Guto Leoni Santos, Patricia Takako Endo, Kayo Henrique De Carvalho Monteiro, Elisson Rocha, Ivanovitch Silva, Theo LynnAbstract:Human Falls are a global public health issue resulting in over 37.3 million severe injuries and 646,000 deaths yearly. Falls result in direct financial cost to health systems and indirectly to society productivity. Unsurprisingly, human Fall Detection and prevention are a major focus of health research. In this article, we consider deep learning for Fall Detection in an IoT and fog computing environment. We propose a Convolutional Neural Network composed of three convolutional layers, two maxpool, and three fully-connected layers as our deep learning model. We evaluate its performance using three open data sets and against extant research. Our approach for resolving dimensionality and modelling simplicity issues is outlined. Accuracy, precision, sensitivity, specificity, and the Matthews Correlation Coefficient are used to evaluate performance. The best results are achieved when using data augmentation during the training process. The paper concludes with a discussion of challenges and future directions for research in this domain.
Jiangpeng Dai - One of the best experts on this subject based on the ideXlab platform.
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mobile phone based pervasive Fall Detection
Ubiquitous Computing, 2010Co-Authors: Jiangpeng Dai, Xiaole Bai, Zhimin Yang, Zhaohui Shen, Dong XuanAbstract:Falls are a major health risk that diminishes the quality of life among the elderly people. The importance of Fall Detection increases as the elderly population surges, especially with aging "baby boomers". However, existing commercial products and academic solutions all Fall short of pervasive Fall Detection. In this paper, we propose utilizing mobile phones as a platform for developing pervasive Fall Detection system. To our knowledge, we are the first to do so. We propose PerFallD, a pervasive Fall Detection system tailored for mobile phones. We design two different Detection algorithms based on the mobile phone platforms for scenarios with and without simple accessories. We implement a prototype system on the Android G1 phone and conduct extensive experiments to evaluate our system. In particular, we compare PerFallD's performance with that of existing work and a commercial product. The experimental results show that PerFallD achieves superior Detection performance and power efficiency.
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perFalld a pervasive Fall Detection system using mobile phones
IEEE International Conference on Pervasive Computing and Communications, 2010Co-Authors: Jiangpeng Dai, Xiaole Bai, Zhimin Yang, Zhaohui Shen, Dong XuanAbstract:Falls are a major health risk that diminish the quality of life among elderly people. With the elderly population surging, especially with aging “baby boomers”, Fall Detection becomes increasingly important. However, existing commercial products and academic solutions struggle to achieve pervasive Fall Detection. In this paper, we propose utilizing mobile phones as a platform for pervasive Fall Detection system development. To our knowledge, we are the first to do so. We design a Detection algorithm based on mobile phone platforms. We propose PerFallD, a pervasive Fall Detection system implemented on mobile phones. We implement a prototype system on the Android G1 phone and conduct experiments to evaluate our system. In particular, we compare PerFallD's performance with that of existing work and a commercial product. Experimental results show that PerFallD achieves strong Detection performance and power efficiency.