Using Sensors to Study Home Activities (original) (raw)
Related papers
Improving activity recognition without sensor data
Proceedings of the 4th Augmented Human International Conference on - AH '13, 2013
Wearable sensing systems, through their proximity with their user, can be used to automatically infer the wearer's activity to obtain detailed information on availability, behavioural patterns and health. For this purpose, classifiers need to be designed and evaluated with sufficient training data from these sensors and from a representative set of users, which requires starting this procedure from scratch for every new sensing system and set of activities. To alleviate this procedure and optimize classification performance, the use of time use surveys has been suggested: These large databases contain typically several days worth of detailed activity information from a large population of hundreds of thousands of participants. This paper uses a strategy first suggested by [16] that utilizes time use diaries in an activity recognition method. We offer a comparison of the aforementioned North-American data with a large European database, showing that although there are several cultural differences, certain important features are shared between both regions. By cross-validating across the 5160 households in this new data with activity episodes of 13798 individuals, especially distinctive features turn out to be time and participant's location. Additionally, we identify for 11 different activities which features are most suited to be used for later on activity recognition.
Recognising Activities at Home
2017
What activities take place at home? When do they occur, for how long do they last and who is involved? Asking such questions is important in social research on households, e.g., to study energyrelated practices, assisted living arrangements and various aspects of family and home life. Common ways of seeking the answers rest on self-reporting which is provoked by researchers (interviews, questionnaires, surveys) or non-provoked (time use diaries). Longitudinal observations are also common, but all of these methods are expensive and time-consuming for both the participants and the researchers. The advances of digital sensors may provide an alternative. For example, temperature, humidity and light sensors report on the physical environment where activities occur, while energy monitors report information on the electrical devices that are used to assist the activities. Using sensor-generated data for the purposes of activity recognition is potentially a very powerful means to study activities at home. However, how can we quantify the agreement between what we detect in sensor-generated data and what we know from self-reported data, especially nonprovoked data? To give a partial answer, we conduct a trial in a household in which we collect data from a suite of sensors, as well as from a time use diary completed by one of the two occupants. For activity recognition using sensor-generated data, we investigate the application of mean shift clustering and change points detection for constructing features that are used to train a Hidden Markov Model. Furthermore, we propose a method for agreement evaluation between the activities detected in the sensor data and that reported by the participants based on the Levenshtein distance. Finally, we analyse the use of di erent features for recognising di erent types of activities.
2015
Smart homes for the aging population have recently started attracting the attention of the research community. The "health state" of smart homes is comprised of many different levels; starting with the physical health of citizens, it also includes longer-term health norms and outcomes, as well as the arena of positive behavior changes. One of the problems of interest is to monitor the activities of daily living (ADL) of the elderly, aiming at their protection and well-being. For this purpose, we installed passive infrared (PIR) sensors to detect motion in a specific area inside a smart apartment and used them to collect a set of ADL. In a novel approach, we describe a technology that allows the ground truth collected in one smart home to train activity recognition systems for other smart homes. We asked the users to label all instances of all ADL only once and subsequently applied data mining techniques to cluster in-home sensor firings. Each cluster would therefore represent the instances of the same activity. Once the clusters were associated to their corresponding activities, our system was able
Key feature identification for recognition of activities performed by a smart-home resident
Journal of Ambient Intelligence and Humanized Computing
Activity recognition is beneficial for continuous health monitoring of smart-home residents, such as patients and elderly people , living in the privacy of their home. We propose an activity recognition approach apposite for a smart home environment. The observations are obtained through multiple sensors deployed at different locations within a smart home. The activities are represented by the features selected from the received observations. The inconsistent order of performing the activities, infrequent occurrences and the presence of overlapping activities make it challenging to select the features with high class representative ability and inter-class discriminative qualities. We select the key features locally within each activity class, which is least affected by the order of performance and the occurrence of other activities. Next, for association of activities, we solve the existing multi-class problem through a specifically designed binary classification with ranking solution, which learns on the correct and incorrect assignments of activities. A comparison of proposed approach with existing methods in terms of recognition accuracy is presented on publicly available 'Kasteren' and 'CASAS' datasets, representing a range of overlapping and well separated activities of daily life. Our approach tailored towards a smart home environment demonstrates a better accuracy than existing methods.
Advances in Intelligent Systems and Computing, 2016
This paper presents a PhD project related to the identification of a set of Activities of Daily Living (ADLs) using different techniques applied to the sensors available in off-the-shelf mobile devices. This project consists on the creation of new methodologies, to identify ADLs, and to present some concepts, such as definition of the set of ADLs relevant to be identified, the mobile device as a multi-sensor system, review of the best techniques for data acquisition, data processing, data validation, data imputation, and data fusion processes, and creation of the methods for the identification of ADLs with data mining, pattern recognition and/or machine learning techniques. However, mobile devices present several limitations, therefore techniques at each stage have to be adapted. As result of this study, we presented a brief review of the state-of-the-art related to the several parts of a mobile-system for the identification of the ADLs. Currently, the main focus consists on the study for the creation of a new method based on the analysis of audio fingerprinting samples in some Ambient Assisted Living (AAL) scenarios.
Towards activity databases: Using sensors and statistical models to summarize people’s lives
2006
Automated reasoning about human behavior is a central goal of artificial intelligence. In order to engage and intervene in a meaningful way, an intelligent system must be able to understand what humans are doing, their goals and intentions. Furthermore, as social animals, people's interactions with each other underlie many aspects of their lives: how they learn, how they work, how they play and how they affect the broader community. Understanding people's interactions and their social networks will play an important role in designing technology and applications that are "socially-aware". This paper introduces some of the current approaches in activity recognition which use a variety of different sensors to collect data about users' activities, and probabilistic models and relational information that are used to transform the raw sensor data into higher-level descriptions of people's behaviors and interactions. The end result of these methods is a richly structured dataset describing people's daily patterns of activities and their evolving social networks. The potential applications of such datasets include mapping patterns of information-flow within an organization, predicting the spread of disease within a community, monitoring the health and activity-levels of elderly patients as well as healthy adults, and allowing "smart environments" to respond proactively to the needs and intentions of their users.
A multi-sensor dataset with annotated activities of daily living recorded in a residential setting
Scientific Data
SPHERE is a large multidisciplinary project to research and develop a sensor network to facilitate home healthcare by activity monitoring, specifically towards activities of daily living. It aims to use the latest technologies in low powered sensors, internet of things, machine learning and automated decision making to provide benefits to patients and clinicians. This dataset comprises data collected from a SPHERE sensor network deployment during a set of experiments conducted in the ‘SPHERE House’ in Bristol, UK, during 2016, including video tracking, accelerometer and environmental sensor data obtained by volunteers undertaking both scripted and non-scripted activities of daily living in a domestic residence. Trained annotators provided ground-truth labels annotating posture, ambulation, activity and location. This dataset is a valuable resource both within and outside the machine learning community, particularly in developing and evaluating algorithms for identifying activities o...
Sensors, 2015
Smart homes for the aging population have recently started attracting the attention of the research community. The "health state" of smart homes is comprised of many different levels; starting with the physical health of citizens, it also includes longer-term health norms and outcomes, as well as the arena of positive behavior changes. One of the problems of interest is to monitor the activities of daily living (ADL) of the elderly, aiming at their protection and well-being. For this purpose, we installed passive infrared (PIR) sensors to detect motion in a specific area inside a smart apartment and used them to collect a set of ADL. In a novel approach, we describe a technology that allows the ground truth collected in one smart home to train activity recognition systems for other smart homes. We asked the users to label all instances of all ADL only once and subsequently applied data mining techniques to cluster in-home sensor firings. Each cluster would therefore represent the instances of the same activity. Once the clusters were associated to their corresponding activities, our system was able
A Comprehensive Study of Activity Recognition Using Accelerometers
This paper serves a survey and empirical evaluation of the state-of-the-art in activity recognition methods using accelerometers. We examine research that has focused on the selection of activities, the features that are extracted from the accelerometer data, the segmentation of the time-series data, the locations of accelerometers, the selection and configuration trade-offs, the test/retest reliability, and the generalisation performance. Furthermore, we study these questions from an experimental platform and show, somewhat surprisingly, that many disparate experimental configurations yield comparable predictive performance on testing data. Our understanding of these results is that the experimental setup directly and indirectly defines a pathway for context to be delivered to the classifier, and that, in some settings, certain configurations are more optimal than alternatives. We conclude by identifying how the main results of this work can be used in practice, specifically in exp...
A practical multi-sensor activity recognition system for home-based care
Decision Support Systems, 2014
To cope with the increasing number of ageing population, a type of care which can help prevent or postpone entry into institutional care is preferable. Activity recognition can be used for home-based care in order to help elderly people to remain at home as long as possible. This paper proposes a practical multi-sensor activity recognition system for home-based care utilizing on-body sensors. Seven types of sensors are investigated on their contributions toward activity classification. We collected a real data set through the experiments participated by a group of elderly people. Seven classification models are developed to explore contribution of each sensor. We conduct a comparison study of four feature selection techniques using the developed models and the collected data. The experimental results show our proposed system is superior to previous works achieving 97% accuracy. The study also demonstrates how the developed activity recognition model can be applied to promote a home-based care and enhance decision support system in health care.