Search results for "Activity recognition"

A 3D Non-Stationary Cluster Channel Model for Human Activity Recognition

2019

Author's accepted manuscript. © 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. This paper proposes a three-dimensional (3D) non- stationary fixed-to-fixed indoor channel simulator model for human activity recognition. The channel model enables the formulation of temporal variations of the received signal caused by a moving human. The moving human is modelled by …

Machine Learning Approaches for Activity Recognition and/or Activity Prediction in Locomotion Assistive Devices—A Systematic Review

2020

Locomotion assistive devices equipped with a microprocessor can potentially automatically adapt their behavior when the user is transitioning from one locomotion mode to another. Many developments in the field have come from machine learning driven controllers on locomotion assistive devices that recognize/predict the current locomotion mode or the upcoming one. This review synthesizes the machine learning algorithms designed to recognize or to predict a locomotion mode in order to automatically adapt the behavior of a locomotion assistive device. A systematic review was conducted on the Web of Science and MEDLINE databases (as well as in the retrieved papers) to identify articles published…

Machine Learning approach towards real time assessment of hand-arm vibration risk

2021

In industry 4,0, the establishment of an interconnected environment where human operators cooperate with the machines offers the opportunity for substantially improving the ergonomics and safety conditions of the workplace. This topic is discussed in the paper referring to the vibration risk, which is a well-known cause of work-related pathologies. A wearable device has been developed to collect vibration data and to segment the signals obtained in time windows. A machine learning classifier is then proposed to recognize the worker’s activity and to evaluate the exposure to vibration risks. The experimental results demonstrate the feasibility and effectiveness of the methodology proposed.

Recognition of Falls and Daily Living Activities Using Machine Learning

2018

A robust fall detection system is essential to support the independent living of elderlies. In this context, we develop a machine learning framework for fall detection and daily living activity recognition. Using acceleration data from public databases, we test the performance of two algorithms to classify seven different activities including falls and activities of daily living. We extract new features from the acceleration signal and demonstrate their effect on improving the accuracy and the precision of the classifier. Our analysis reveals that the quadratic support vector machine classifier achieves an overall accuracy of 93.2% and outperforms the artificial neural network algorithm. Re…

Mobile Phone Data Statistics as Proxy Indicator for Regional Economic Activity Assessment

2019

The mobile data analysis is an authoritative source of information for problems solving in the fields of human activity recognition, population dynamics, tourism, transport planning, traffics measuring, public administration and other activities and could be the source for valuable information as a proxy indicator. One of the obstacles to user data from mobile operators is compliance to the General Data Protection Regulation, so the development of data analytics approach that protects personal data without a necessity to identify mobility of particular persons was developed, that still provides economically relevant data. In the present research, the method for the economic activity assessm…

A Trajectory-Driven SISO mm-Wave Channel Model for a Human Activity Recognition

2021

ActRec: A Wi-Fi-Based Human Activity Recognition System

2020

In this paper, we develop a Wi-Fi-based activity recognition system called ActRec, which can be used for the remote monitoring of elderly. ActRec comprises two parts: radio-frequency (RF) sensing and machine learning. In the RF sensing part, two laptops act as transmitter and receiver to record the channel transfer function of an indoor environment. This RF data is collected in the presence of seven human participants performing three activities: walking, falling, and sitting. The RF data containing the fingerprints of user activity is then pre-processed with various signal processing algorithms to reduce noise effects and to estimate the mean Doppler shift (MDS) of each data sample. We pro…

KARD - Kinect Activity Recognition Dataset

2017

To cite this dataset, please refer to the following paper:Human Activity Recognition Process Using 3-D Posture Data. S. Gaglio, G. Lo Re, M. Morana. In IEEE Transactions on Human-Machine Systems. 2014 doi: 10.1109/THMS.2014.2377111******************************************************************KARD contains 18 Activities. Each activity is performed 3 times by 10 different subjects.1Horizontal arm wave2High arm wave3Two hand wave4Catch Cap5High throw6Draw X7Draw Tick8Toss Paper9Forward Kick10Side Kick11Take Umbrella12Bend13Hand Clap14Walk15Phone Call16Drink17Sit down18Stand upIn total, you have 4 (files) x 18 (activities) x 3 (repetitions) x 10 (subjects), that is 2160 files.Each filename …

Motion sensors for activity recognition in an ambient-intelligence scenario

2013

In recent years, Ambient Intelligence (AmI) has attracted a number of researchers due to the widespread diffusion of unobtrusive sensing devices. The availability of such a great amount of acquired data has driven the interest of the scientific community in producing novel methods for combining raw measurements in order to understand what is happening in the monitored scenario. Moreover, due the primary role of the end user, an additional requirement of any AmI system is to maintain a high level of pervasiveness. In this paper we propose a method for recognizing human activities by means of a time of flight (ToF) depth and RGB camera device, namely Microsoft Kinect. The proposed approach is…

Fall Detection Based on the Instantaneous Doppler Frequency : A Machine Learning Approach

2019

Modern societies are facing an ageing problem which comes with increased cost of healthcare. A major share of this ever-increasing cost is due to fall related injuries, which urges the development of fall detection systems. In this context, this paper paves the way for building of a radio-frequency-based fall detection system. This paper presents an activity simulator that generates the complex channel gain of indoor channels in the presence of one person performing three different activities, namely, slow fall, fast fall, and walking. We built a machine learning framework for activity recognition based on the complex channel gain. We assess the recognition accuracy of three different class…