Out-of-home activity analysis using a low-resolution visual sensor

Loneliness and social isolation are probably the most prevalent psychosocial problems related to aging. One critical component in assessing social isolation in an unobtrusive manner is to measure the out-of-home activity levels, as social isolation often goes along with decreased physical activity, decreased motoric functioning, and a decline in activities of daily living, all of which may lead to a reduction in the amount of time spent out-of-home. In this work, we propose to use a single visual sensor for detecting out-of-home activity. The visual sensor has a very low spatial resolution (900 pixels), which is a key feature to ensure a cheap technology and to maintain the user’s privacy. Firstly, the visual sensor is installed in a top view setup at the door entrance. Secondly, a correlation-based foreground detection method is used to extract the foreground. Thirdly, an Extra Trees Classifier (ETC) is trained to classify the directionality of the person (in/out) based on the motion of the foreground pixels. Due to the nature of variability of the out-of-home activity, the relative frequency of the directionality (in/out) is measured over a window of 3 seconds to determine the final result. We installed our system in 9 different service flats in the UK, Belgium and France where the same ETC model is used. We evaluate our method on video sequences captured in real-life environments from the different setups, where the persons’ out-of-home routines are recorded. The results show that our approach of detecting out-of-home activity achieves an accuracy of 91.30%

Behavior analysis for aging-in-place using similarity heatmaps

The demand for healthcare services for an increasing population of older adults is faced with the shortage of skilled caregivers and a constant increase in healthcare costs. In addition, the strong preference of the elderly to live independently has been driving much research on "ambient-assisted living" (AAL) systems to support aging-in-place. In this paper, we propose to employ a low-resolution image sensor network for behavior analysis of a home occupant. A network of 10 low-resolution cameras (30x30 pixels) is installed in a service flat of an elderly, based on which the user's mobility tracks are extracted using a maximum likelihood tracker. We propose a novel measure to find similar patterns of behavior between each pair of days from the user's detected positions, based on heatmaps and Earth mover's distance (EMD). Then, we use an exemplar-based approach to identify sleeping, eating, and sitting activities, and walking patterns of the elderly user for two weeks of real-life recordings. The proposed system achieves an overall accuracy of about 94%

Detection of visitors in elderly care using a low-resolution visual sensor network

Loneliness is a common condition associated with aging and comes with extreme health consequences including decline in physical and mental health, increased mortality and poor living conditions. Detecting and assisting lonely persons is therefore important-especially in the home environment. The current studies analyse the Activities of Daily Living (ADL) usually with the focus on persons living alone, e.g., to detect health deterioration. However, this type of data analysis relies on the assumption of a single person being analysed, and the ADL data analysis becomes less reliable without assessing socialization in seniors for health state assessment and intervention. In this paper, we propose a network of cheap low-resolution visual sensors for the detection of visitors. The visitor analysis starts by visual feature extraction based on foreground/background detection and morphological operations to track the motion patterns in each visual sensor. Then, we utilize the features of the visual sensors to build a Hidden Markov Model (HMM) for the actual detection. Finally, a rule-based classifier is used to compute the number and the duration of visits. We evaluate our framework on a real-life dataset of ten months. The results show a promising visit detection performance when compared to ground truth

A low-cost visual sensor network for elderly care

A low-resolution visual sensor network enables monitoring of elderly people's health and safety at home, postponing institutionalized healthcare

Design, synthesis, and biological activity of novel heptacyclic pyrazolamide derivatives : a new candidate of dual-target insect growth regulators

Insect growth regulators (IGRs) can cause abnormal growth and development in insects, resulting in incomplete metamorphosis or even death of the larvae. Ecdysone receptor (EcR) and chitinase in insects play indispensable roles in the molting process. Ecdysone analogues and chitinase inhibitors are considered as potential IGRs. In order to find new and highly effective IGR candidates, based on the structure-activity relationship and molecular docking results of the active compound 6i (3-(tert-butyl)-N-(4-(tert-butyl)phenyl)-1-phenyl-1H-pyrazole-5-carboxamide) discovered in our previous work, we changed the t-butyl group on the pyrazole ring into heptacycle to enhance the hydrophobicity. Consequently, a series of novel heptacyclic pyrazolamide derivatives were designed and synthesized. The bioassay results demonstrated that some compounds showed obvious insecticidal activity. Especially, D-27 (N-(4-(tert-butyl)phenyl)-2-phenyl-2,4,5,6,7,8-hexahydrocyclohepta[c]pyrazole-5-carboxamide) showed good activities against Plutella xylostella (LC50, 51.50 mg.L-1) and Mythimna separata (100% mortality at 2.5 mg.L-1). Furthermore, protein validation indicated that D-27 acts not only on the EcR but also on chitinase Of ChtI. Molecular docking and molecular dynamics simulation explained the vital factors in the interaction between D-27 and receptors. D-27 may be a new lead candidate with a dual target in which Of ChtI shall be the main one. This work created a new starting point for discovering a novel type of IGRs

Sleep analysis for elderly care using a low-resolution visual sensor network

Nearly half of the senior citizens report difficulty initiating and maintaining sleep. Frequent visits to the bathroom in the middle of the night is considered as one of the major reasons for sleep disorder. This leads to serious diseases such as depression and diabetes. In this paper, we propose to use a network of cheap low-resolution visual sensors (30 x 30 pixels) for long-term activity analysis of a senior citizen in a service flat. The main focus of our research is on elderly behaviour analysis to detect health deterioration. Specifically, this paper treats the analysis of sleep patterns. Firstly, motion patterns are detected. Then, a rule-based approach on the motion patterns is proposed to determine the wake up time and sleep time. The nightly bathroom visit is identified using a classification-based model. In our evaluation, we performed experiments on 10 months of real-life data. The ground truth is collected from the diaries in which the senior citizen wrote down his sleep time and wake up time. The results show accurate extraction of the sleep durations with an overall Mean Absolute Error (MAE) of 22.91 min and Spearman correlation coefficient of 0.69. Finally, the nightly bathroom visits analysis indicate sleep disorder in several nights

Discovering activity patterns in office environment using a network of low-resolution visual sensors

Understanding activity patterns in office environments is important in order to increase workers’ comfort and productivity. This paper proposes an automated system for discovering activity patterns of multiple persons in a work environment using a network of cheap low-resolution visual sensors (900 pixels). Firstly, the users’ locations are obtained from a robust people tracker based on recursive maximum likelihood principles. Secondly, based on the users’ mobility tracks, the high density positions are found using a bivariate kernel density estimation. Then, the hotspots are detected using a confidence region estimation. Thirdly, we analyze the individual’s tracks to find the starting and ending hotspots. The starting and ending hotspots form an observation sequence, where the user’s presence and absence are detected using three powerful Probabilistic Graphical Models (PGMs). We describe two approaches to identify the user’s status: a single model approach and a two-model mining approach. We evaluate both approaches on video sequences captured in a real work environment, where the persons’ daily routines are recorded over 5 months. We show how the second approach achieves a better performance than the first approach. Routines dominating the entire group’s activities are identified with a methodology based on the Latent Dirichlet Allocation topic model. We also detect routines which are characteristic of persons. More specifically, we perform various analysis to determine regions with high variations, which may correspond to specific events

Parametric tracking with spatial extraction across an array of cameras

Video surveillance is a rapidly growing area that has been fuelled by an increase in the concerns of security and safety in both public and private areas. With heighten security concerns, the utilization of video surveillance systems spread over a large area is becoming the norm. Surveillance of a large area requires a number of cameras to be deployed, which presents problems for human operators. In the surveillance of a large area, the need to monitor numerous screens makes an operator less effective in monitoring, observing or tracking groups or targets of interest. In such situations, the application of computer systems can prove highly effective in assisting human operators. The overall aim of this thesis was to investigate different methods for tracking a target across an array of cameras. This required a set of parameters to be identified that could be passed between cameras as the target moved in and out of the fields of view. Initial investigations focussed on identifying the most effective colour space to use. A normalized cross correlation method was used initially with a reference image to track the target of interest. A second method investigated the use of histogram similarity in tracking targets. In this instance a reference target’s histogram or pixel distribution was used as a means for tracking. Finally a method was investigated that used the relationship between colour regions that make up a whole target. An experimental method was developed that used the information between colour regions such as the vector and colour difference as a means for tracking a target. This method was tested on a single camera configuration and multiple camera configuration and shown to be effective. In addition to the experimental tracking method investigated, additional data can be extracted to estimate a spatial map of a target as the target of interest is tracked across an array of cameras. For each method investigated the experimental results are presented in this thesis and it has been demonstrated that minimal data exchange can be used in order to track a target across an array of cameras. In addition to tracking a target, the spatial position of the target of interest could be estimated as it moves across the array