Sensor Data Simulation for Anomaly Detection of the Elderly Living Alone
- URL: http://arxiv.org/abs/2312.16852v1
- Date: Thu, 28 Dec 2023 06:38:52 GMT
- Title: Sensor Data Simulation for Anomaly Detection of the Elderly Living Alone
- Authors: Kai Tanaka, Mineichi Kudo, and Keigo Kimura
- Abstract summary: There is a growing demand for sensor-based detection of anomalous behaviors.
There is a problem of lack of sufficient real data for developing detection algorithms.
We propose a novel sensor data simulator that can model these factors in generation of sensor data.
- Score: 0.49157446832511503
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: With the increase of the number of elderly people living alone around the
world, there is a growing demand for sensor-based detection of anomalous
behaviors. Although smart homes with ambient sensors could be useful for
detecting such anomalies, there is a problem of lack of sufficient real data
for developing detection algorithms. For coping with this problem, several
sensor data simulators have been proposed, but they have not been able to model
appropriately the long-term transitions and correlations between anomalies that
exist in reality. In this paper, therefore, we propose a novel sensor data
simulator that can model these factors in generation of sensor data. Anomalies
considered in this study were classified into three types of \textit{state
anomalies}, \textit{activity anomalies}, and \textit{moving anomalies}. The
simulator produces 10 years data in 100 min. including six anomalies, two for
each type. Numerical evaluations show that this simulator is superior to the
past simulators in the sense that it simulates well day-to-day variations of
real data.
Related papers
- Long-term Detection System for Six Kinds of Abnormal Behavior of the Elderly Living Alone [1.4061979259370274]
We propose a simulator-based detection system for six typical anomalies: being semi-bedridden, being housebound, forgetting, wandering, fall while walking and fall while standing.
Our detection system can be customized for various room layout, sensor forgetting arrangement and resident's characteristics.
We propose a method that standardizes the processing of sensor data, and uses a simple detection approach.
arXiv Detail & Related papers (2024-11-20T09:42:08Z) - Are we certain it's anomalous? [57.729669157989235]
Anomaly detection in time series is a complex task since anomalies are rare due to highly non-linear temporal correlations.
Here we propose the novel use of Hyperbolic uncertainty for Anomaly Detection (HypAD)
HypAD learns self-supervisedly to reconstruct the input signal.
arXiv Detail & Related papers (2022-11-16T21:31:39Z) - DynImp: Dynamic Imputation for Wearable Sensing Data Through Sensory and
Temporal Relatedness [78.98998551326812]
We argue that traditional methods have rarely made use of both times-series dynamics of the data as well as the relatedness of the features from different sensors.
We propose a model, termed as DynImp, to handle different time point's missingness with nearest neighbors along feature axis.
We show that the method can exploit the multi-modality features from related sensors and also learn from history time-series dynamics to reconstruct the data under extreme missingness.
arXiv Detail & Related papers (2022-09-26T21:59:14Z) - Sensing Anomalies as Potential Hazards: Datasets and Benchmarks [43.55994393060723]
We consider the problem of detecting, in the visual sensing data stream of an autonomous mobile robot, semantic patterns that are unusual.
We contribute three novel image-based datasets acquired in robot exploration scenarios.
We study the performance of an anomaly detection approach based on autoencoders operating at different scales.
arXiv Detail & Related papers (2021-10-27T18:47:06Z) - DAE : Discriminatory Auto-Encoder for multivariate time-series anomaly
detection in air transportation [68.8204255655161]
We propose a novel anomaly detection model called Discriminatory Auto-Encoder (DAE)
It uses the baseline of a regular LSTM-based auto-encoder but with several decoders, each getting data of a specific flight phase.
Results show that the DAE achieves better results in both accuracy and speed of detection.
arXiv Detail & Related papers (2021-09-08T14:07:55Z) - Bayesian Autoencoders for Drift Detection in Industrial Environments [69.93875748095574]
Autoencoders are unsupervised models which have been used for detecting anomalies in multi-sensor environments.
Anomalies can come either from real changes in the environment (real drift) or from faulty sensory devices (virtual drift)
arXiv Detail & Related papers (2021-07-28T10:19:58Z) - Deep Visual Anomaly detection with Negative Learning [18.79849041106952]
In this paper, we propose anomaly detection with negative learning (ADNL), which employs the negative learning concept for the enhancement of anomaly detection.
The idea is to limit the reconstruction capability of a generative model using the given a small amount of anomaly examples.
This way, the network not only learns to reconstruct normal data but also encloses the normal distribution far from the possible distribution of anomalies.
arXiv Detail & Related papers (2021-05-24T01:48:44Z) - Discriminative-Generative Dual Memory Video Anomaly Detection [81.09977516403411]
Recently, people tried to use a few anomalies for video anomaly detection (VAD) instead of only normal data during the training process.
We propose a DiscRiminative-gEnerative duAl Memory (DREAM) anomaly detection model to take advantage of a few anomalies and solve data imbalance.
arXiv Detail & Related papers (2021-04-29T15:49:01Z) - Convolutional Neural Networks for Sleep Stage Scoring on a Two-Channel
EEG Signal [63.18666008322476]
Sleep problems are one of the major diseases all over the world.
Basic tool used by specialists is the Polysomnogram, which is a collection of different signals recorded during sleep.
Specialists have to score the different signals according to one of the standard guidelines.
arXiv Detail & Related papers (2021-03-30T09:59:56Z) - Unsupervised Abnormality Detection Using Heterogeneous Autonomous
Systems [0.3867363075280543]
Anomaly detection in a surveillance scenario is an emerging and challenging field of research.
In this paper, a heterogeneous system is proposed which estimates the degree of abnormality of an unmanned surveillance drone.
The proposed method performs satisfactorily on the IEEE SP Cup-2020 dataset with an accuracy of 97.3%.
arXiv Detail & Related papers (2020-06-05T23:09:58Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.