Online Guest Detection in a Smart Home using Pervasive Sensors and Probabilistic Reasoning

• URL: http://arxiv.org/abs/2003.06347v1
• Date: Fri, 13 Mar 2020 15:41:15 GMT
• Title: Online Guest Detection in a Smart Home using Pervasive Sensors and Probabilistic Reasoning
• Authors: Jennifer Renoux, Uwe K\"ockemann, Amy Loutfi
• Abstract summary: This paper presents a probabilistic approach able to estimate the number of persons in the environment at each time step. Using both simulated and real data, our method has been tested and validated on two smart homes of different sizes and configuration.
• Score: 3.538944147459101
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Smart home environments equipped with distributed sensor networks are capable of helping people by providing services related to health, emergency detection or daily routine management. A backbone to these systems relies often on the system's ability to track and detect activities performed by the users in their home. Despite the continuous progress in the area of activity recognition in smart homes, many systems make a strong underlying assumption that the number of occupants in the home at any given moment of time is always known. Estimating the number of persons in a Smart Home at each time step remains a challenge nowadays. Indeed, unlike most (crowd) counting solution which are based on computer vision techniques, the sensors considered in a Smart Home are often very simple and do not offer individually a good overview of the situation. The data gathered needs therefore to be fused in order to infer useful information. This paper aims at addressing this challenge and presents a probabilistic approach able to estimate the number of persons in the environment at each time step. This approach works in two steps: first, an estimate of the number of persons present in the environment is done using a Constraint Satisfaction Problem solver, based on the topology of the sensor network and the sensor activation pattern at this time point. Then, a Hidden Markov Model refines this estimate by considering the uncertainty related to the sensors. Using both simulated and real data, our method has been tested and validated on two smart homes of different sizes and configuration and demonstrates the ability to accurately estimate the number of inhabitants.

Related papers

• Layout Agnostic Human Activity Recognition in Smart Homes through Textual Descriptions Of Sensor Triggers (TDOST) [0.22354214294493352]
  We develop a layout-agnostic modeling approach for human activity recognition (HAR) systems in smart homes. We generate Textual Descriptions Of Sensor Triggers (TDOST) that encapsulate the surrounding trigger conditions. We demonstrate the effectiveness of TDOST-based models in unseen smart homes through experiments on benchmarked CASAS datasets.
  arXiv  Detail & Related papers  (2024-05-20T20:37:44Z)
• Know Thy Neighbors: A Graph Based Approach for Effective Sensor-Based Human Activity Recognition in Smart Homes [0.0]
  We propose a novel graph-guided neural network approach for Human Activity Recognition (HAR) in smart homes. We accomplish this by learning a more expressive graph structure representing the sensor network in a smart home. Our approach maps discrete input sensor measurements to a feature space through the application of attention mechanisms.
  arXiv  Detail & Related papers  (2023-11-16T02:43:13Z)
• Agile gesture recognition for capacitive sensing devices: adapting on-the-job [55.40855017016652]
  We demonstrate a hand gesture recognition system that uses signals from capacitive sensors embedded into the etee hand controller. The controller generates real-time signals from each of the wearer five fingers. We use a machine learning technique to analyse the time series signals and identify three features that can represent 5 fingers within 500 ms.
  arXiv  Detail & Related papers  (2023-05-12T17:24:02Z)
• To Compute or not to Compute? Adaptive Smart Sensing in Resource-Constrained Edge Computing [1.7361161778148904]
  We consider a network of smart sensors for an edge computing application that sample a time-varying signal and send updates to a base station for remote global monitoring. Sensors are equipped with sensing and compute, and can either send raw data or process them on-board before transmission. We propose an estimation-theoretic optimization framework that embeds both computation and communication latency.
  arXiv  Detail & Related papers  (2022-09-05T23:46:42Z)
• Bayesian Imitation Learning for End-to-End Mobile Manipulation [80.47771322489422]
  Augmenting policies with additional sensor inputs, such as RGB + depth cameras, is a straightforward approach to improving robot perception capabilities. We show that using the Variational Information Bottleneck to regularize convolutional neural networks improves generalization to held-out domains. We demonstrate that our method is able to help close the sim-to-real gap and successfully fuse RGB and depth modalities.
  arXiv  Detail & Related papers  (2022-02-15T17:38:30Z)
• Benchmarking high-fidelity pedestrian tracking systems for research, real-time monitoring and crowd control [55.41644538483948]
  High-fidelity pedestrian tracking in real-life conditions has been an important tool in fundamental crowd dynamics research. As this technology advances, it is becoming increasingly useful also in society. To successfully employ pedestrian tracking techniques in research and technology, it is crucial to validate and benchmark them for accuracy. We present and discuss a benchmark suite, towards an open standard in the community, for privacy-respectful pedestrian tracking techniques.
  arXiv  Detail & Related papers  (2021-08-26T11:45:26Z)
• Domain and Modality Gaps for LiDAR-based Person Detection on Mobile Robots [91.01747068273666]
  This paper studies existing LiDAR-based person detectors with a particular focus on mobile robot scenarios. Experiments revolve around the domain gap between driving and mobile robot scenarios, as well as the modality gap between 3D and 2D LiDAR sensors. Results provide practical insights into LiDAR-based person detection and facilitate informed decisions for relevant mobile robot designs and applications.
  arXiv  Detail & Related papers  (2021-06-21T16:35:49Z)
• Feeling of Presence Maximization: mmWave-Enabled Virtual Reality Meets Deep Reinforcement Learning [76.46530937296066]
  This paper investigates the problem of providing ultra-reliable and energy-efficient virtual reality (VR) experiences for wireless mobile users. To ensure reliable ultra-high-definition (UHD) video frame delivery to mobile users, a coordinated multipoint (CoMP) transmission technique and millimeter wave (mmWave) communications are exploited.
  arXiv  Detail & Related papers  (2021-06-03T08:35:10Z)
• Energy Aware Deep Reinforcement Learning Scheduling for Sensors Correlated in Time and Space [62.39318039798564]
  We propose a scheduling mechanism capable of taking advantage of correlated information. The proposed mechanism is capable of determining the frequency with which sensors should transmit their updates. We show that our solution can significantly extend the sensors' lifetime.
  arXiv  Detail & Related papers  (2020-11-19T09:53:27Z)
• Data-Driven Distributed State Estimation and Behavior Modeling in Sensor Networks [5.817715558396024]
  We formulate the problem of simultaneous state estimation and behavior learning in a sensor network. We propose a simple yet effective solution by extending the Gaussian process-based Bayes filters (GP-BayesFilters) to an online, distributed setting. The effectiveness of the proposed method is evaluated on tracking objects with unknown movement behaviors using both synthetic data and data collected from a multi-robot platform.
  arXiv  Detail & Related papers  (2020-09-22T21:31:18Z)
• Wavelet-based temporal models of human activity for anomaly detection in smart robot-assisted environments [2.299866262521074]
  This paper presents a new approach for temporal modelling of long-term human activities with smart-home sensors. The model is based on wavelet transforms and used to forecast smart sensor data, providing a temporal prior to detect unexpected events in human environments.
  arXiv  Detail & Related papers  (2020-02-26T14:08:46Z)

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.