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Due to the low cost and good privacy protection of binary sensors, there are many applications of binary sensors. To enhance the spatial awareness of binary sensors, researchers utilize modulators to modulate views of sensors into visible and invisible regions so that the monitoring space is segmented into small cells identified by signatures. When a warm object moves in these cells, its location or moving trajectory can be acquired more accurately with modulators than without modulators. Accordingly, the maximum number of cells (MNC) in a deployment determines partially the maximal spatial awareness of the binary sensor system. In this paper, we provide a theoretical study of the MNC, given the number of sensors and the number of modulators. We also find the sufficient and necessary conditions to achieve the MNC so that we provide the reasons why deployment cannot obtain the MNC. These conditions can guide researchers to design the MNC deployments. Furthermore, we provide a method to calculate the number of cells when a deployment sometime cannot obtain the MNC. Our experiments provide deep insights into the influences of those conditions on the MNC and the MNC on the spatial awareness of binary sensor systems.

Continual learning is an emerging research challenge in human activity recognition (HAR). As an increasing number of HAR applications are deployed in real-world environments, it is important and essential to extend the activity model to adapt to the change in people’s activity routine. Otherwise, HAR applications can become obsolete and fail to deliver activity-aware services. The existing research in HAR has focused on detecting abnormal sensor events or new activities, however, extending the activity model is currently under-explored. To directly tackle this challenge, we build on the recent advance in the area of lifelong machine learning and design a continual activity recognition system, called HAR-GAN , to grow the activity model over time. HAR-GAN does not require a prior knowledge on what new activity classes might be and it does not require to store historical data by leveraging the use of Generative Adversarial Networks (GAN) to generate sensor data on the previously learned activities. We have evaluated HAR-GAN on four third-party, public datasets collected on binary sensors and accelerometers. Our extensive empirical results demonstrate the effectiveness of HAR-GAN in continual activity recognition and shed insight on the future challenges.

International audience; This paper studies the identification of infinite impulse response (IIR) systems with binary-valued measurements. The key to our approach is to recast the initial identification problem into an identification problem in the set-membership framework. This enables us to identify the system using an identification algorithm devoted to IIR systems in such a framework. Although the proposed algorithm is designed for noise free data, an analysis is provided in the presence of noise, and illustrative examples are provided to demonstrate the effectiveness of the algorithm.

Unobtrusive activity recognition is known to be the most preferred solution for monitoring daily activities of elderly people. In this paper, Scanpath Trend Analysis (STA) is employed for unobtrusive activity recognition of elderly people living alone. Binary sensor data are used and each activity is considered as a sequence of sensor points for this purpose. The real-world long-term fully annotated Aruba open dataset collected by binary sensors is used for the verification of accuracy and the efficacy of the proposed approach. With the STA, the F1-score of 0.758 is obtained, and furthermore, by adding some extra semantic information through an activity transition matrix, it is possible to have F1-score as 0.863. This F1-score is superior to all the related works that use binary sensor data for activity prediction, while computationally the approach presented is advantageous since long periods of training process can be avoided.

Producción Científica, In recent years, Artificial Intelligence Technologies (AIT) have been developed to improve the quality of life of the elderly and their safety in the home. This work focuses on developing a system capable of recognising the most usual activities in the daily life of an elderly person in real-time to enable a specialist to monitor the habits of this person, such as taking medication or eating the correct meals of the day. To this end, a prediction model has been developed based on recurrent neural networks, specifically on bidirectional LSTM networks, to obtain in real-time the activity being carried out by the individuals in their homes, based on the information provided by a set of different sensors installed at each person’s home. The prediction model developed in this paper provides a 95.42% accuracy rate, improving the results of similar models currently in use. In order to obtain a reliable model with a high accuracy rate, a series of processing and filtering processes have been carried out on the data, such as a method based on a sliding window or a stacking and re-ordering algorithm, that are subsequently used to train the neural network, obtained from the public database CASAS., Ministerio de Ciencia, Innovación y Universidades (project RTI2018-096652-B-I00), Junta de Castilla y León - Fondo Europeo de Desarrollo Regional (project VA233P18)

In this paper we propose a novel method for location recognition and prediction in smart homes based on semi-supervised learning. We use data collected from low-resolution depth video cameras installed in four apartments with older adults over 70 years of age, and collected during a period of one to seven weeks. The location of the person in the depth images is detected by a person detection algorithm adapted from YOLO (You Only Look Once). The locations extracted from the videos are then clustered using K-means clustering. Sequence prediction algorithms are used to predict the next cluster (location) based on the previous clusters (locations). The accuracy of predicting the next location is up to 91%, a significant improvement compared to the case where binary sensors are placed in the apartment based on human intuition. The paper presents an analysis on the effect of the memory length (i.e. the number of previous clusters used to predict the next one), and on the amount of recorded data required to converge.

This article is concerned with the secure particle filtering problem for a class of discrete-time nonlinear cyber-physical systems with binary sensors in the presence of non-Gaussian noises and multiple malicious attacks. The multiple attacks launched by the adversaries, which take place in a random manner, include the denial-of-service attacks, the deception attacks, and the flipping attacks. Three sequences of Bernoulli-distributed random variables with known probability distributions are employed to describe the characteristics of the random occurrence of the multiple attacks. The raw or corrupted measurements are transmitted to sensors, whose outputs are binary according to engineering practice. A modified likelihood function is constructed to compensate for the influence of the randomly occurring multiple attacks by introducing the random occurrence probability information into the design process. Subsequently, a secure particle filter is proposed based on the constructed likelihood function. Finally, a moving target tracking application is elaborated to verify the viability of the proposed secure particle filtering algorithm.

Aging is a global challenge facing our society in the next few decades. Ambient assisted living (AAL) is a promising technology that helps people stay active, socially connected, and independent into older age. Even though ambient binary sensors, such as passive infrared (PIR) motion sensors, offer a low cost, easy to deploy, and less intrusive solution to constructing a smart environment, the limited ability of coping with multiple residents hinders the wide adoption of the AAL technology. In this work, we present three multiresident tracking algorithms, nearest neighbor with sensor graph (NN-SG), global nearest neighbor with sensor graph (GNN-SG), and multiresident tracking with sensor vectorization (sMRT), to solve the data association problem between the ambient sensor events and residents in the smart environment. We also introduce new performance metrics to evaluate the success of alternative approaches to multiresident tracking in smart homes. We evaluate all the algorithms with a recent smart home dataset recorded in real-life settings. Among the three algorithms, NN-SG and GNN-SG require sensor location and floor plan of the environment to derive the sensor graph, while sMRT does not require such information and relies solely on the unannotated sensor data. As an initiative of the unsupervised resident tracking solution, sMRT prompts additional research opportunities in multiresident tracking to improve the adoption of AAL technology in our daily life.

Many elderly persons prefer to stay alone in a single-resident house for seeking an independent life and reducing the cost of health care. However, the independent life cannot be maintained if the resident develops dementia. Thus, an early detection of dementia is essential for the elderly to extend their independent lifetime. Early symptoms of dementia can be noticed in everyday activities such as front-door events. For example, forgetting something when the person leaves the house might be an early symptom of dementia. In this paper, we introduce a novel front-door events [exit, enter, visitor, other, and brief-return-and-exit (BRE)] classification scheme that validated by using open data sets ( $n =14$ ) collected from 14 testbeds by anonymous wireless binary sensors (passive infrared sensors and magnetic sensors). BRE events occur when four consecutive events (exit-enter-exit-enter) happen in certain time intervals ( $t_{1}$ , $t_{2}$ , and $t_{3})$ , and some of them may be the forget events. Each testbed had one older adult (aged 73 and over) during the experimental period ( $\mu = 547.6 \pm 370.4$ days). The algorithm automatically classifies the resident’s front-door events. Experimental results show the events of total exits, daily exits, out-time per exit, as well as the significance of the $\text{t}_{\mathrm {i}}$ parameters for the number of classified BRE events. Since part of the BRE events may be the forget events, the proposed algorithm could be a useful tool for the forget event detection.

We present a comprehensive study of state-of-the-art algorithms for the prediction of sensor events and activities of daily living in smart homes. Data have been collected from eight smart homes with real users and 13-17 binary sensors each – including motion, magnetic, and power sensors. We apply two probabilistic methods, namely Sequence Prediction via Enhanced Episode Discovery and Active LeZi, as well as Long Short-Term Memory Recurrent Neural Network, in order to predict the next sensor event in a sequence. We compare these with respect to the required number of preceding sensor events to predict the next, the necessary amount of data to achieve good accuracy and convergence, as well as varying the number of sensors in the dataset. The best-performing method is further improved by including information on the time of occurrence to predict the next sensor event only, and in addition to predict both the next sensor event and the mean time of occurrence in the same model. Subsequently, we apply transfer learning across apartments to investigate its applicability, advantages, and limitations for this setup. Our best implementation achieved an accuracy of 77-87% for predicting the next sensor event, and an accuracy of 73-83% when predicting both the next sensor event and the mean time elapsed to the next sensor event. Finally, we investigate the performance of predicting daily living activities derived from the sensor events. We can predict activities with an accuracy of 61-90%, depending on the apartment. This work was supported by the Norwegian Research Council through the SAMANSVAR programme under Grant 247620/O70.

Low cost robots, such as vacuum cleaners or lawn mowers, employ simplistic and often random navigation policies. Although a large number of sophisticated localization and planning approaches exist, they require additional sensors like LIDAR sensors, cameras or time of flight sensors. In this work, we propose a global localization method biologically inspired by simple insects, such as the ant Cataglyphis that is able to return from distant locations to its nest in the desert without any or with limited perceptual cues. Like in Cataglyphis, the underlying idea of our localization approach is to first compute a pose estimate from pro-prioceptual sensors only, using land navigation, and thereafter refine the estimate through a systematic search in a particle filter that integrates the rare visual feedback. In simulation experiments in multiple environments, we demonstrated that this bioinspired principle can be used to compute accurate pose estimates from binary visual cues only. Such intelligent localization strategies can improve the performance of any robot with limited sensing capabilities such as household robots or toys., Accepted to BIOSIGNALS 2019

In this paper, we present work in progress on activity recognition and prediction in real homes using either binary sensor data or depth video data. We present our field trial and set-up for collecting and storing the data, our methods, and our current results. We compare the accuracy of predicting the next binary sensor event using probabilistic methods and Long Short-Term Memory (LSTM) networks, include the time information to improve prediction accuracy, as well as predict both the next sensor event and its time of occurrence using one LSTM model. We investigate transfer learning between apartments and show that it is possible to pre-train the model with data from other apartments and achieve good accuracy in a new apartment straight away. In addition, we present preliminary results from activity recognition using low resolution depth video data from seven apartments, and classify four activities – no movement, standing up, sitting down, and TV interaction – by using a relatively simple processing method where we apply an Infinite Impulse Response (IIR) filter to extract movements from the frames prior to feeding them to a convolutional LSTM network for the classification. Financed by the Norwegian Research Council under the SAMANSVAR programme (247620/O70).

In spite of the fact that many sensors in use today are binary (i.e. produce only values of 0 and 1), and that useful context-aware applications are built exclusively on top of them, there is currently no development approach specifically targeted to binary sensors. Dealing with notions of state and state combinators, central to binary sensors, is tedious and error-prone in current approaches. For instance, developing such applications in a general programming language requires writing code to process events, maintain state and perform state transitions on events, manage timers and/or event histories. In another paper, we introduced a domain specific language (DSL) called Allen, specifically targeted to binary sensors. Allen natively expresses states and state combinations, and detects contexts on line, on incoming streams of binary events. Expressing state combinations in Allen is natural and intuitive due to a key ingredient: semi-causal operators. That paper focused on the concept of the language and its main operators, but did not address its implementation challenges. Indeed, online evaluation of expressions containing semi-causal operators is difficult, because semi-causal sub-expressions may block waiting for future events, thus generating unknown values, besides 0 and 1. These unknown values may or may not propagate to the containing expressions, depending on the current value of the other arguments. This paper presents a compiler and runtime for the Allen language, and shows how they implement its state combining operators, based on reducing complex expressions to a core subset of operators, which are implemented natively. We define several assisted living applications both in Allen and in a general scripting language. We show that the former are much more concise in Allen, achieve more effective code reuse, and ease the checking of some domain properties.

Many real-world applications, which are focused on addressing the needs of a human, require information pertaining to the activities being performed. The UCAmI Cup is an event held within the context of the International Conference on Ubiquitous Computing and Ambient Intelligence, where delegates are given the opportunity to use their tools and techniques to analyse a previously unseen human activity recognition dataset and to compare their results with others working in the same domain. In this paper, the human activity recognition dataset used relates to activities of daily living generated in the UJAmI Smart Lab, University of Jaén. The dataset chosen for the first edition of the UCAmI Cup represents 246 activities performed over a period of ten days carried out by a single inhabitant. The dataset includes four data sources: (i) event streams from 30 binary sensors, (ii) intelligent floor location data, (iii) proximity data between a smart watch worn by the inhabitant and 15 Bluetooth Low Energy beacons and (iv) acceleration of the smart watch. In this first edition of the UCAmI Cup, 26 participants from 10 different countries contacted the organizers to obtain the dataset.‬‬‬‬‬

Activities of Daily Living (ADLs), or a person’s routine activities of self-care, are important factors influencing the feasibility of home health care or aging in place for many individuals. Automated, sensor-based recognition of such activities affords home stay, greater independence and privacy, and improved quality of life to individuals who would require stay in a supervised or medical facility. This paper describes a data-driven framework for the design and deployment of such an automated system for activity recognition using simple, unobtrusive, and privacy-friendly binary sensors. It presents the results of an experimental study, with both numerical and qualitative observations, of this framework on a publicly available real dataset.

Although there have been many studies aimed at the field of Human Activity Recognition, the relationship between what we do and where we do it has been little explored in this field. The objective of this paper is to propose an approach based on machine learning to address the challenge of the 1st UCAmI cup, which is the recognition of 24 activities of daily living using a dataset that allows to explore the aforementioned relationship, since it contains data collected from four data sources: binary sensors, an intelligent floor, proximity and acceleration sensors. The methodology for data mining projects CRISP-DM was followed in this work. To perform synchronization and classification tasks a java desktop application was developed. As a result, the accuracy achieved in the classification of the 24 activities using 10-fold-cross-validation on the training dataset was 92.1%, but an accuracy of 60.1% was obtained on the test dataset. The low accuracy of the classification might be caused by the class imbalance of the training dataset; therefore, more labeled data are necessary for training the algorithm. Although we could not obtain an optimal result, it is possible to iterate in the methodology to look for a way to improve the obtained results.

This short paper presents the activity recognition results obtained from the CAR-CSIC team for the UCAmI’18 Cup. We propose a multi-event naive Bayes classifier for estimating 24 different activities in real-time. We use all the sensorial information provided for the competition, i.e., binary sensors fixed to everyday objects, proximity BLE-based tags, location-aware smart floor sensing and the wrist’s acceleration. The results using training data-sets of 7 days show accuracies (true positives) about 68%; however for the three extra data-sets of the competition we were able to reach a 60.5% accuracy.

The challenge of recognizing different personal activities while living in an apartment is of great interest for the AAL community. Many different approaches have been presented trying to achieve good accuracies in activity recognition, combined with different heuristics, windowing and segmentation methods. In this paper we want to revisit the basic methodology proposed by a naive Bayes implementation with emphasis on multi-type event-driven location-aware activity recognition. Our method combines multiple events generated by binary sensors fixed to everyday objects, a capacitive smart floor, the received signal strength (RSS) from BLE beacons to a smart-watch and the sensed acceleration of the actor’s wrist. Our new method does not use any segmentation phase, it interprets the received events as soon as they are measured and activity estimations are generated in real-time without any post-processing or time-reversal re-estimation. An activity prediction model is used in order to guess the more-likely next activity to occur. The evaluation results show an improved performance when adding new sensor type events to the activity engine estimator. Classification results achieve accuracies of about 68%, which is a good figure taking into account the high number of different activities to classify (24).

We present preliminary results on sensor data prediction in a smart home environment with a limited number of binary sensors. The data has been collected from a real home with one resident over a period of 17 weeks. We apply Recurrent Neural Network with Long Short-Term Memory to a text sequence derived from the sensors’ events to predict the next event in a sequence. We compare our system’s characteristics and results to a baseline method and to similar work in the area. Our implementation achieved a peak accuracy of 69% for a set with 13 sensors in total - motion, magnetic and power sensors - and 75% for five motion sensors. Financed by the Norwegian Research Council under the SAMANSVAR programme (247620/O70).

Detecting activities of daily living (ADL) allows for rich inference about user behavior, which can be of use in the care of for example, elderly people, chronic diseases, and psychological conditions. This paper proposes a domain knowledge-based solution for detecting 24 different ADLs in the UJA dataset. The solution is inspired by a Finite State Machine and performs activity recognition unobtrusively using only binary sensors. Each day in the dataset is segmented into: morning, day, evening in order to facilitate the inference from the sensors. The model performs the ADL recognition in two steps. The first step is to detect the sequence of activities in a given event stream of binary sensors, and the second step is to assign a starting and ending times for each of detected activities. Our proposed model achieved an accuracy of 81.3% using only a very small amount of operations, making it an interesting approach for resource-constrained devices that are common in smart environments. It should be noted, however, that the model can end up in faulty states which could cause a series of mis-classifications before the model is returned to the true state.

This paper considers the problem of localising a signal source using a team of mobile agents that can only detect the presence or absence of the signal. A background false detection rate and missed detection probability are incorporated into the assumptions. An estimation algorithm is proposed that discretizes the search environment into cells, and uses Bayesian techniques to approximate the posterior probability of each cell containing the source. Analytical results are presented for a range of specific cases, and simulations are used to investigate more complex scenarios.

The early detection of dementia is crucial in independent life style of elderly people. Main intention of this study is to propose device-free non-privacy invasive Deep Convolutional Neural Network classifier (DCNN) for Martino-Saltzman's (MS) travel patterns of elderly people living alone using open dataset collected by binary (passive infrared) sensors. Travel patterns are classified as direct, pacing, lapping, or random according to MS model. MS travel pattern is highly related with person's cognitive state, thus can be used to detect early stage of dementia. The dataset was collected by monitoring a cognitively normal elderly resident by wireless passive infrared sensors for 21 months. First, over 70000 travel episodes are extracted from the dataset and classified by MS travel pattern classifier algorithm for the ground truth. Later, 12000 episodes (3000 for each pattern) were randomly selected from the total episodes to compose training and testing dataset. Finally, DCNN performance was compared with three other classical machine-learning classifiers. The Random Forest and DCNN yielded the best classification accuracies of 94.48% and 97.84%, respectively. Thus, the proposed DCNN classifier can be used to infer dementia through travel pattern matching.

In the past few decades, the number of elderly people who prefer to live independently is significantly increasing among the elderly people due to the issues of privacy invasion and elderly care cost. Device-free non-privacy invasive activity recognition is preferred for long-term monitoring. Thus, we propose a deep learning classification method for elderly activities using binary sensors (PIR sensor and door sensor). In particular, we present a Deep Convolutional Neural Network (DCNN) classification approach for detecting four basic activity classes which represent the basic human activities in a home monitoring environment, namely: Bed_to_Toilet, Eating, Meal_Preparation, and Relax. A real-world long-term annotated dataset is employed for evaluation of the activity recognition classifier. Dataset was offered by Center for Advanced Studies in Adaptive Systems (CASAS) project, and was collected by monitoring a cognitively normal elderly resident by binary sensors for 21 months First, we converted the annotated binary sensor data into a binary activity images for corresponding activities. Then, activity images are used for training and testing the DCNN classifier. Finally, classifiers are evaluated with 10-fold cross validation method. Experimental results showed the best DCNN classifier gives 99.36% of accuracy. Our next step is to improve this classifier for detection of intertwined complex activities of elderly and to implement it on a real life long-term elderly monitoring system.

Many elderly persons prefer to stay alone in a single-resident house for seeking an independent life and reducing the cost of health care. However, the independent life cannot be maintained if the resident develops dementia. Thus, an early detection of dementia is essential for the elderly to extend their independent lifetime. One of the early symptoms of dementia is forgetting something when the person leaves the house. In this study, we introduce a novel front-door events (exit, enter, visitor, other, and brief-return-and-exit (BRE)) and their classification scheme that validated by using open datasets (n = 10) collected from ten single-resident testbeds by anonymous binary sensors. BRE events occur when four consecutive events (exit-enter-exit-enter) happen in some certain time intervals (t1, t2, and t3), and some of them may be the forget events. Each testbed had one older adult (aged 73 years and over) during the experimental period (μ = 583.1 ± 297.3 days). The algorithm automatically classifies the resident's front-door events and ignores visitor's entrance and exit events. The experimental results reveal the significance of the tiparameters for the number of BRE events. Since BRE events may include forget events, the proposed algorithm could be a useful tool for the forget event detection.

According to the World Health Organization and the US Department of Health, older adults are one of the most rapidly expanding population segments of many developed countries. As a result, significant research in the area of assistive technologies for the elderly has been undertaken to help manage this growing demographic segment. Our contribution to this effort is based on leveraging environmental sensors to track occupants for extended durations in their homes in order to extract and track various behavioral patterns in order to identify atypical activities. To aid in our detection of behavioral patterns, we extract additional intermediate indicators of mobility and behavior such as estimates of an occupant’s position as well as other parameters such as guest visitations. In our initial research, we did not associate these instances of atypical behaviors with any specific health events. Our primary goal was to see if it was possible to detect atypical behaviors using only the limited data that can be extracted using passive infrared sensors. We compared the days when atypical patterns were detected with the similar analysis of a human annotated activity log and determined that there is a strong correlation between patterns extracted from our derived behavioral indicators and those from the annotated activities. We also compared our systems result’s across multiple datasets to verify that our approach could be applied to different sensor configurations and occupants.

Most expert projections indicate that in 2030, there will be over one billion people aged 60 or over. The vast majority of them prefer to spend their last years at home, and almost a third of them live alone. This creates a growing need for technology-based solutions capable of helping older people to live independently in their places. Despite the wealth of solutions proposed for this general problem, there are very few support systems that can be reproduced on a larger scale. In this study, we propose a method to monitor the activity of the elderly living alone and detect deviations from the previous activity patterns based on the idea that the residential living environment can be modeled as a collection of behaviorally significant places located arbitrarily in a generic space. Then we use virtual pheromones—a concept defined in our previous work—to create images of the pheromone distribution maps, which describe the spatiotemporal evolution of the interactions between the user and the environment. We propose a method to detect deviations from the activity routines based on a simple statistical analysis of the resulting images. By applying this method on two public activity recognition datasets, we found that the system is capable of detecting both singular deviations and slow-deviating trends from the previous activity routine of the monitored persons.

With a rising ageing population, smart home technologies have been demonstrated as a promising paradigm to enable technology-driven healthcare delivery. Smart home technologies, composed of advanced sensing, computing, and communication technologies, offer an unprecedented opportunity to keep track of behaviours and activities of the elderly and provide context-aware services that enable the elderly to remain active and independent in their own homes. However, experiments in developed prototypes demonstrate that abnormal sensor events hamper the correct identification of critical (and potentially life-threatening) situations, and that existing learning, estimation, and time-based approaches to situation recognition are inaccurate and inflexible when applied to multiple people sharing a living space. We propose a novel technique, called CLEAN, that integrates the semantics of sensor readings with statistical outlier detection. We evaluate the technique against four real-world datasets across different environments including the datasets with multiple residents. The results have shown that CLEAN can successfully detect sensor anomaly and improve activity recognition accuracies. Postprint

We present an evaluation of two approaches to the problem of inferring the presence of multiple persons in networks of binary sensors. This problem is critical for many applications of Ambient Assisted Living that benefit from knowledge of single- and multi-person presence where data is collected using ambient sensors. Both approaches make use of a graph representing sensors and their spatial relations. One approach uses a simple statistical method to derive a minimum number of people present, the other precisely tracks people through the sensor network. Both approaches are evaluated in a low and higher resolution setting on data of two persons inhabiting a laboratory equipped with motion sensors and contact sensors. Although the latter approach performs well tracking multiple persons, its inability to distinguish inactivity and absence make the former approach more suitable for this task, independent of resolution.

International audience; This correspondence paper deals with the sensor placement optimization problem in the context of indoor multiple inhabitants location tracking to solve ambient assisted living problems. Binary sensors, like passive infrared (PIR) sensors, are used to guaranty specific coverage requirements and allow privacy respecting. Moreover, within real home environments, different kinds of obstacles (like walls, high furniture , etc.) can affect the detection capacity of PIR sensors. This paper proposes an integrated framework devoted to optimize the placement of sensors and PIR sensors in smart homes by taking into account physical topologies and coverage precision constraints. An integer linear programming problem is formalized and a case study illustrates the applicability of the proposed approach and the scalability of the optimization method. Index Terms—Ambient assisted living (AAL), binary sensors, integer linear programming (ILP), smart home.

System identification is investigated for plants that are equipped with only binary-valued sensors. Optimal identification errors, time complexity, optimal input design, and impact of disturbances and unmodeled dynamics on identification accuracy and complexity are examined in both stochastic and deterministic information frameworks. It is revealed that binary sensors impose fundamental limitations on identification accuracy and time complexity, and carry distinct features beyond identification with regular sensors. Comparisons between the stochastic and deterministic frameworks indicate a complementary nature in their utility in binary-sensor identification.

Experiments in assisted living confirm that such systems can provide context-aware services that enable occupants to remain active and independent. They also demonstrate that abnormal sensor events hamper the correct identification of critical (and potentially life-threatening) situations, and that existing learning, estimation, and time-based approaches are inaccurate and inflexible when applied to multiple people sharing a living space. We propose a technique that integrates the semantics of sensor readings with statistical outlier detection. We evaluate the technique against four real-world datasets that include multiple individuals, and show consistent rates of anomaly detection across different environments. Postprint

In this paper, we propose a spatial and temporal flying target detection method using different types of sensors. By combining the sensors, we can discover the hit time and location more precisely. We designed ta system composition in consideration of protecting sensor system, and implemented a working prototype. In the experiments, we demonstrate its performance depending on the processing module and the accuracy.

The aging population is continuously growing and this results in increasing the demands for using technologies to help to manage the rapidly growing sector of the elderly population. To contribute in this effort, we propose a method that can find similar patterns of behavior for extended durations. Our method uses motion sensors as a privacy-aware alternative to cameras. We compute three initial parameters to extract similar patterns of behavior: (1) movement in spot; (2) movement between rooms; and (3) movement within rooms. The three parameters demonstrate good similarity indicators for finding patterns of behavior between each pair of days.

In this paper, we propose a method for estimating the number of pedestrians walking in opposite directions, as in cases of a shopping street or a sidewalk in a downtown area. The proposed method utilizes a compound-eye sensor that is constructed by placing two binary sensors for the pedestrians’ movement direction and multiple binary sensors for the vertical direction of the pedestrians’ movement direction. A number of Monte Carlo simulations about the movement of pedestrians are conducted, and the output history of the compound-eye sensor is obtained in each simulation. The simulation scenario with a small difference of the output history of the compound-eye sensor is selected to estimate the number of pedestrians. Evaluation results show that in the field whose width is 8 [m] the relative error in the proposed method is the smallest by using 2×8 binary sensors.

One of the most appealing applications of wireless sensor networks (WSNs) is in human detection and tracking. The aim of these applications is to detect if a person is in an area of interest, and to keep track of his location at every instant of time. In recent years, we have seen a growing interest in the development of proposals for the use of WSNs in detection and tracking applications for healthcare. In the particular case of a patient suffering from dementia, it is very important to detect him and keep track of his location at every time, to avoid that the patient may enter to a zone of risk without supervision. When an event of interest is detected, such as wandering, an action may be taken by sending out a notification to the caregiver personnel. In this chapter, we review the most important proposals regarding the use of WSNs for human detection and tracking in healthcare applications. Moreover, we introduce a model for detection of patients suffering from dementia, based on a WSN that uses binary sensors. The proposed model is able to detect if a patient leaves a secure zone without supervision, and to emit alerts directed to caregivers.

Ambient Intelligent (AmI) technology can be used to help older adults to live longer and independent lives in their own homes. Information collected from AmI environment can be used to detect and understanding human behaviour, allowing personalized care. The behaviour pattern can also be used to detect changes in behaviour and predict future trends, so that preventive action can be taken. However, due to the large number of sensors in the environment, sensor data are often complex and difficult to interpret, especially to capture behaviour trends and to detect changes over the long-term. In this paper, a model to predict the indoor mobility using binary sensors is proposed. The model utilizes weekly routine to predict the future trend. The proposed method is validated using data collected from a real home environment, and the results show that using weekly pattern helps improve indoor mobility prediction. Also, a new measurement, Mobility Entropy (ME), to measure indoor mobility based on entropy concept is proposed. The results indicate ME can be used to distinguish elders with different mobility and to see decline in mobility. The proposed work would allow detection of changes in mobility, and to foresee the future mobility trend if the current behaviour continues.

This paper addresses the observability and independence of a system monitored using networked distributed sensors under the assumption that some sensing results are indiscriminable. We theoretically analyze the independence of sensing templates, that is, whether sensing systems are not redundant, and the observability of such systems, that is, whether we can determine the state of a system from the sensing results. Application examples to which the results given in this paper can be applied are: (1) end-to-end network quality monitored by end users in which the route actually used is not known and (2) object detection/non-detection by binary sensors randomly deployed without positioning mechanisms.

We propose an algorithm for shape recognition of the target object using the notion of the inner distance. The inner distance of the object going across the field, where binary sensors are densely deployed, are obtained from the distance between two binary sensors concurrently detecting the object. To estimate the position of each binary sensor, we apply a range-free localization using a moving landmark, which wanders around the field of watch. We numerically study the accuracy of the shape recognition based on the inner distance when it is combinedly used with the range-free sensor localization based on the moving landmark.

International audience; This paper studies the problem of associating deterministically a track revealed by a binary sensor network with the trajectory of a unique moving anonymous object, namely the \emph{Multiple Object Tracking and Identification} (MOTI) problem. In our model, the network is represented by a sparse connected graph where each vertex represents a binary sensor and there is an edge between two sensors if an object can pass from a sensed region to another without activating any other remaining sensor. The difficulty of MOTI lies in the fact that trajectories of two or more objects can be so close (track merging) that the corresponding tracks on the sensor network can no longer be distinguished, thus confusing the deterministic association between an object trajectory and a track. The paper presents several results. We first show that MOTI cannot be solved on a general graph of ideal binary sensors even by an omniscient external observer if all the objects can freely move on the graph. Then, we describe some restrictions that can be imposed \emph{a priori} either on the graph, on the object movements or both, to make MOTI problem always solvable. We also discuss the consequences of our results and present some related open problems.

Publisher Summary This chapter provides an insight into the actuator sensor interface (AS-i). The AS-i is an open system network developed by eleven manufacturers who created the AS-i association to develop the AS-i specifications. Some of the more famous members of the association include Pepperl-Fuchs, Allen-Bradley, Banner Engineering, Datalogic Products, Siemens, Telemecanique, Turck, Omron, Eaton, and Festo. AS-i is a bit-oriented communication link, designed to connect binary sensors and actuators. Most of these devices do not require multiple bytes to adequately convey the necessary information about the device status, so the AS-i communication interface is designed for bit-oriented messages in order to increase message efficiency for these types of devices. AS-i uses a two-wire untwisted, unshielded cable that serves as both communication link and power supply for up to thirty-one slaves. A single master module controls communication over the AS-i network, which can be connected in various configurations such as bus, ring, or tree. The data-link layer of the AS-i network consists of a master call-up and slave response. The master call-up is exactly fourteen bits in length while the slave response is 7 bits.