Pattern Activities Identification in the Framework of Medical Nursing Home Using Infrared Sensors (original) (raw)
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2018
Improvement of life quality in the developed nations has systematically generated an increase in the life expectancy. A statistic studies curried out by the French national institute of statistic and economic studies (INSEE) shows a new distribution of age classes in France. In fact, almost one in three people will be over 60 years in 2050, against one in five in 2005, and France will have over 10 million of people over 75 years and over 4 million of people over 85 years. Nevertheless, the increasing number of elderly person implies more resources for aftercare, paramedical care and natural assistance in their habitats. The current healthcare infrastructure in those countries is widely considered to be inadequate to meet the needs of this increasingly older population. In this case a permanent assistance is necessary wherever they are, healthcare monitoring is a solution to deal with this problem and ensure the elderly to live safely and independently in their own home for as long a...
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Abnormal Activity Detection Using Pyroelectric Infrared Sensors
Sensors (Basel, Switzerland), 2016
Healthy aging is one of the most important social issues. In this paper, we propose a method for abnormal activity detection without any manual labeling of the training samples. By leveraging the Field of View (FOV) modulation, the spatio-temporal characteristic of human activity is encoded into low-dimension data stream generated by the ceiling-mounted Pyroelectric Infrared (PIR) sensors. The similarity between normal training samples are measured based on Kullback-Leibler (KL) divergence of each pair of them. The natural clustering of normal activities is discovered through a self-tuning spectral clustering algorithm with unsupervised model selection on the eigenvectors of a modified similarity matrix. Hidden Markov Models (HMMs) are employed to model each cluster of normal activities and form feature vectors. One-Class Support Vector Machines (OSVMs) are used to profile the normal activities and detect abnormal activities. To validate the efficacy of our method, we conducted expe...
Monitoring of the daily living activities in smart home care
Human-centric Computing and Information Sciences, 2017
One of the key requirements for technological systems that are used to secure independent housing for seniors in their home environment is monitoring of daily living activities (ADL), their classification, and recognition of routine daily patterns and habits of seniors in Smart Home Care (SHC). To monitor daily living activities, the use of a temperature, CO2, humidity sensors, and microphones are described in experiments in this study. The first part of the paper describes the use of CO2 concentration measurement for detecting and monitoring room´s occupancy in SHC. In second part focuses this paper on the proposal of an implementation of Artificial Neural Network based on the Levenberg–Marquardt algorithm (LMA) for the detection of human presence in a room of SHC with the use of predictive calculation of CO2 concentrations from obtained measurements of temperature (indoor, outdoor) Ti, To and relative air humidity rH. Based on the long-term monitoring (1 month) of operational and ...