1,523 research outputs found
Unsupervised routine discovery in egocentric photo-streams
The routine of a person is defined by the occurrence of activities throughout
different days, and can directly affect the person's health. In this work, we
address the recognition of routine related days. To do so, we rely on
egocentric images, which are recorded by a wearable camera and allow to monitor
the life of the user from a first-person view perspective. We propose an
unsupervised model that identifies routine related days, following an outlier
detection approach. We test the proposed framework over a total of 72 days in
the form of photo-streams covering around 2 weeks of the life of 5 different
camera wearers. Our model achieves an average of 76% Accuracy and 68% Weighted
F-Score for all the users. Thus, we show that our framework is able to
recognise routine related days and opens the door to the understanding of the
behaviour of people
Learning Human Behaviour Patterns by Trajectory and Activity Recognition
The world’s population is ageing, increasing the awareness of neurological and behavioural
impairments that may arise from the human ageing. These impairments can be manifested
by cognitive conditions or mobility reduction. These conditions are difficult to be
detected on time, relying only on the periodic medical appointments. Therefore, there is
a lack of routine screening which demands the development of solutions to better assist
and monitor human behaviour. The available technologies to monitor human behaviour
are limited to indoors and require the installation of sensors around the user’s homes
presenting high maintenance and installation costs. With the widespread use of smartphones,
it is possible to take advantage of their sensing information to better assist the
elderly population. This study investigates the question of what we can learn about human
pattern behaviour from this rich and pervasive mobile sensing data. A deployment
of a data collection over a period of 6 months was designed to measure three different
human routines through human trajectory analysis and activity recognition comprising
indoor and outdoor environment. A framework for modelling human behaviour was
developed using human motion features, extracted in an unsupervised and supervised
manner. The unsupervised feature extraction is able to measure mobility properties such
as step length estimation, user points of interest or even locomotion activities inferred
from an user-independent trained classifier. The supervised feature extraction was design
to be user-dependent as each user may have specific behaviours that are common to
his/her routine. The human patterns were modelled through probability density functions
and clustering approaches. Using the human learned patterns, inferences about
the current human behaviour were continuously quantified by an anomaly detection
algorithm, where distance measurements were used to detect significant changes in behaviour.
Experimental results demonstrate the effectiveness of the proposed framework
that revealed an increase potential to learn behaviour patterns and detect anomalies
An unsupervised behavioral modeling and alerting system based on passive sensing for elderly care
Artificial Intelligence in combination with the Internet of Medical Things enables remote healthcare services through networks of environmental and/or personal sensors. We present a remote healthcare service system which collects real-life data through an environmental sensor package, including binary motion, contact, pressure, and proximity sensors, installed at households of elderly people. Its aim is to keep the caregivers informed of subjects’ health-status progressive trajectory, and alert them of health-related anomalies to enable objective on-demand healthcare service delivery at scale. The system was deployed in 19 households inhabited by an elderly person with post-stroke condition in the Emilia–Romagna region in Italy, with maximal and median observation durations of 98 and 55 weeks. Among these households, 17 were multi-occupancy residences, while the other 2 housed elderly patients living alone. Subjects’ daily behavioral diaries were extracted and registered from raw sensor signals, using rule-based data pre-processing and unsupervised algorithms. Personal behavioral habits were identified and compared to typical patterns reported in behavioral science, as a quality-of-life indicator. We consider the activity patterns extracted across all users as a dictionary, and represent each patient’s behavior as a ‘Bag of Words’, based on which patients can be categorized into sub-groups for precision cohort treatment. Longitudinal trends of the behavioral progressive trajectory and sudden abnormalities of a patient were detected and reported to care providers. Due to the sparse sensor setting and the multi-occupancy living condition, the sleep profile was used as the main indicator in our system. Experimental results demonstrate the ability to report on subjects’ daily activity pattern in terms of sleep, outing, visiting, and health-status trajectories, as well as predicting/detecting 75% hospitalization sessions up to 11 days in advance. 65% of the alerts were confirmed to be semantically meaningful by the users. Furthermore, reduced social interaction (outing and visiting), and lower sleep quality could be observed during the COVID-19 lockdown period across the cohort
Artificial Intelligence based Anomaly Detection of Energy Consumption in Buildings: A Review, Current Trends and New Perspectives
Enormous amounts of data are being produced everyday by sub-meters and smart
sensors installed in residential buildings. If leveraged properly, that data
could assist end-users, energy producers and utility companies in detecting
anomalous power consumption and understanding the causes of each anomaly.
Therefore, anomaly detection could stop a minor problem becoming overwhelming.
Moreover, it will aid in better decision-making to reduce wasted energy and
promote sustainable and energy efficient behavior. In this regard, this paper
is an in-depth review of existing anomaly detection frameworks for building
energy consumption based on artificial intelligence. Specifically, an extensive
survey is presented, in which a comprehensive taxonomy is introduced to
classify existing algorithms based on different modules and parameters adopted,
such as machine learning algorithms, feature extraction approaches, anomaly
detection levels, computing platforms and application scenarios. To the best of
the authors' knowledge, this is the first review article that discusses anomaly
detection in building energy consumption. Moving forward, important findings
along with domain-specific problems, difficulties and challenges that remain
unresolved are thoroughly discussed, including the absence of: (i) precise
definitions of anomalous power consumption, (ii) annotated datasets, (iii)
unified metrics to assess the performance of existing solutions, (iv) platforms
for reproducibility and (v) privacy-preservation. Following, insights about
current research trends are discussed to widen the applications and
effectiveness of the anomaly detection technology before deriving future
directions attracting significant attention. This article serves as a
comprehensive reference to understand the current technological progress in
anomaly detection of energy consumption based on artificial intelligence.Comment: 11 Figures, 3 Table
Sensor-based datasets for human activity recognition - a systematic review of literature
The research area of ambient assisted living has led to the development of activity recognition
systems (ARS) based on human activity recognition (HAR). These systems improve the quality of life and
the health care of the elderly and dependent people. However, before making them available to end users, it is
necessary to evaluate their performance in recognizing activities of daily living, using data set benchmarks
in experimental scenarios. For that reason, the scientific community has developed and provided a huge
amount of data sets for HAR. Therefore, identifying which ones to use in the evaluation process and which
techniques are the most appropriate for prediction of HAR in a specific context is not a trivial task and
is key to further progress in this area of research. This work presents a systematic review of the literature
of the sensor-based data sets used to evaluate ARS. On the one hand, an analysis of different variables
taken from indexed publications related to this field was performed. The sources of information are journals,
proceedings, and books located in specialized databases. The analyzed variables characterize publications
by year, database, type, quartile, country of origin, and destination, using scientometrics, which allowed
identification of the data set most used by researchers. On the other hand, the descriptive and functional
variables were analyzed for each of the identified data sets: occupation, annotation, approach, segmentation,
representation, feature selection, balancing and addition of instances, and classifier used for recognition.
This paper provides an analysis of the sensor-based data sets used in HAR to date, identifying the most
appropriate dataset to evaluate ARS and the classification techniques that generate better results
Mobility increases localizability: A survey on wireless indoor localization using inertial sensors
Wireless indoor positioning has been extensively studied for the past 2 decades and continuously attracted growing research efforts in mobile computing context. As the integration of multiple inertial sensors (e.g., accelerometer, gyroscope, and magnetometer) to nowadays smartphones in recent years, human-centric mobility sensing is emerging and coming into vogue. Mobility information, as a new dimension in addition to wireless signals, can benefit localization in a number of ways, since location and mobility are by nature related in the physical world. In this article, we survey this new trend of mobility enhancing smartphone-based indoor localization. Specifically, we first study how to measure human mobility: what types of sensors we can use and what types of mobility information we can acquire. Next, we discuss how mobility assists localization with respect to enhancing location accuracy, decreasing deployment cost, and enriching location context. Moreover, considering the quality and cost of smartphone built-in sensors, handling measurement errors is essential and accordingly investigated. Combining existing work and our own working experiences, we emphasize the principles and conduct comparative study of the mainstream technologies. Finally, we conclude this survey by addressing future research directions and opportunities in this new and largely open area.</jats:p
Sensor-based datasets for human activity recognition - a systematic review of literature
The research area of ambient assisted living has led to the development of activity recognition
systems (ARS) based on human activity recognition (HAR). These systems improve the quality of life and
the health care of the elderly and dependent people. However, before making them available to end users, it is
necessary to evaluate their performance in recognizing activities of daily living, using data set benchmarks
in experimental scenarios. For that reason, the scientific community has developed and provided a huge
amount of data sets for HAR. Therefore, identifying which ones to use in the evaluation process and which
techniques are the most appropriate for prediction of HAR in a specific context is not a trivial task and
is key to further progress in this area of research. This work presents a systematic review of the literature
of the sensor-based data sets used to evaluate ARS. On the one hand, an analysis of different variables
taken from indexed publications related to this field was performed. The sources of information are journals,
proceedings, and books located in specialized databases. The analyzed variables characterize publications
by year, database, type, quartile, country of origin, and destination, using scientometrics, which allowed
identification of the data set most used by researchers. On the other hand, the descriptive and functional
variables were analyzed for each of the identified data sets: occupation, annotation, approach, segmentation,
representation, feature selection, balancing and addition of instances, and classifier used for recognition.
This paper provides an analysis of the sensor-based data sets used in HAR to date, identifying the most
appropriate dataset to evaluate ARS and the classification techniques that generate better results
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