Search results for "activity recognition"
showing 10 items of 42 documents
Decoding Children's Social Behavior
2013
We introduce a new problem domain for activity recognition: the analysis of children's social and communicative behaviors based on video and audio data. We specifically target interactions between children aged 1-2 years and an adult. Such interactions arise naturally in the diagnosis and treatment of developmental disorders such as autism. We introduce a new publicly-available dataset containing over 160 sessions of a 3-5 minute child-adult interaction. In each session, the adult examiner followed a semi-structured play interaction protocol which was designed to elicit a broad range of social behaviors. We identify the key technical challenges in analyzing these behaviors, and describe met…
A Trajectory-Driven 3D Channel Model for Human Activity Recognition
2021
This paper concerns the design, analysis, and simulation of a 3D non-stationary channel model fed with inertial measurement unit (IMU) data. The work in this paper provides a framework for simulating the micro-Doppler signatures of indoor channels for human activity recognition by using radiofrequency-based sensing technologies. The major human body segments, such as wrists, ankles, torso, and head, are modelled as a cluster of moving point scatterers. We provide expressions for the time variant (TV) speed and TV angles of motion based on 3D trajectories of the moving person. Moreover, we present mathematical expressions for the TV Doppler shifts and TV path gains associated with each movin…
The Influence of Human Walking Activities on the Doppler Characteristics of Non-stationary Indoor Channel Models
2019
This paper analyzes the time-variant (TV) Doppler power spectral density of a 3D non-stationary fixed-to-fixed indoor channel simulator after feeding it with realistic trajectories of a walking person. The trajectories of the walking person are obtained by simulating a full body musculoskeletal model in OpenSim. We provide expressions of the TV Doppler frequencies caused by these trajectories. Then, we present the complex channel gain consisting of fixed scatterers and a cluster of moving scatterers. After that, we use the concept of the spectrogram to analyze the TV Doppler power spectral density of the complex channel gain. Finally, we present expressions of the TV mean Doppler shift and …
The Impact of Human Walking on the Time-Frequency Distribution of In-Home Radio Channels
2018
Passive activity recognition of home occupants has become a very hot topic in the area of radio communications, as it enables the development of cutting-edge healthcare monitoring solutions. Thanks to ubiquitous radio waves, such as WiFi signals, at today's homes, one can process radio waves reflected off a person's body for identifying certain mobility patterns. This new approach ignores the need for any wearable sensors. This paper reports a challenging indoor radio channel measurement campaign at 5.9 GHz, which has been conducted to study the impact of walking persons on the temporal and spectral properties of the channel. In particular, the time-frequency distribution of the channel has…
RF-Based Human Activity Recognition: A Non-stationary Channel Model Incorporating the Impact of Phase Distortions
2019
This paper proposes a non-stationary channel model that captures the impact of the time-variant (TV) phase distortion caused by hardware imperfections. The model allows for studying the spectrogram of in-home radio channels influenced by walking activities of the home user under realistic non-stationary propagation conditions. The resolution of the spectrogram is investigated for the von-Mises distribution of the phase distortion. It is shown that high-entropy distributions considerably mask fingerprints of the user activity on the spectrogram of the channel. For an orthogonal frequency-division multiplexing (OFDM) system, a computationally simple method for mitigating the undesired phase r…
Wi-Sense: a passive human activity recognition system using Wi-Fi and convolutional neural network and its integration in health information systems
2021
AbstractA human activity recognition (HAR) system acts as the backbone of many human-centric applications, such as active assisted living and in-home monitoring for elderly and physically impaired people. Although existing Wi-Fi-based human activity recognition methods report good results, their performance is affected by the changes in the ambient environment. In this work, we present Wi-Sense—a human activity recognition system that uses a convolutional neural network (CNN) to recognize human activities based on the environment-independent fingerprints extracted from the Wi-Fi channel state information (CSI). First, Wi-Sense captures the CSI by using a standard Wi-Fi network interface car…
Managing sensor data streams in a smart home application
2020
A challenge in developing an ambient activity recognition system for use in elder care is finding a balance between the sophistication of the system and a cost structure that fits within the budgets of public and private sector healthcare organisations. Much activity recognition research in the context of elder care is based on dense networks of sensors and advanced methods, such as supervised machine learning algorithms. This paper presents the data processing aspects of an activity recognition system based on a simpler, knowledge-based unsupervised approach, designed for a sparse network of sensors. By structuring sensor data management as a streaming system, we provide a simple programmi…
Publish-subscribe smartphone sensing platform for the acute phase of a disaster: A framework for emergency management support
2014
The advanced sensors embedded in modern smartphones opens up for novel research opportunities, as for instance manifested in the field of mobile phone sensing. Most notable is perhaps research activities within human activity recognition and context-aware applications. Along a similar vein, the SmartRescue project targets monitoring of both hazard developments as well as tracking of people in a disaster, taking advantage of smartphone sensing, processing and communication capabilities. The goal is to help crisis managers and the public in early hazard detection, hazard prediction, and in the forming of risk minimizing evacuation plans when disaster strikes. In this paper we propose a novel …
User Activity Recognition for Energy Saving in Smart Homes
2015
Abstract Energy demand in typical home environments accounts for a significant fraction of the overall consumption in industrialized countries. In such context, the heterogeneity of the involved devices, and the non negligible influence of the human factor make the optimization of energy use a challenging task; effective automated approaches must take into account basic information about users, such as the prediction of their course of actions. Our proposal consists in learning customized structural models for common user activities for predicting the trend of energy consumption; the approach aims to lower energy demand in the proximity of predicted peak loads so as to keep the overall cons…
A System for Simultaneous People Tracking and Posture Recognition in the context of Human-Computer Interaction
2005
The paper deals with an artificial-vision based system for simultaneous people tracking and posture recognition In the context of human-computer Interaction. We adopt no particular assumptions on the movement of a person and on Its appearance, making the system suitable to several real-world applications. The system can be roughly subdivided Into two highly correlated phases: tracking and recognition. The tracking phase Is concerned with establishing coherent relations of the same subject between frames. We adopted the Condensation algorithm due to Its robustness In highly cluttered environments. The recognition phase adopts a modified elgenspace technique In order to classify between sever…