Human activity recognition based on feature selection in smart home using back-propagation algorithm

Fang, Hongqing; He, Lei; Si, Hao; Liu, Peng; Xie, Xiaolei

doi:10.1016/j.isatra.2014.06.008

Cited by 63 publications

(35 citation statements)

References 34 publications

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“…To further analyze the validity of SVM recognition, the PCA features are taken as the input, and the same samples are used for recognition based on SVM and BP Neural Network (BPNN) . The results are shown in Fig.…”

Section: Experiments and Resultsmentioning

confidence: 99%

Detection and recognition of human body posture in motion based on sensor technology

Zhao

Chen

2020

IEEJ Transactions Elec Engng

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The recognition of human motion posture is of great values in the field of sports. In this paper, inertial sensor technology is employed to recognize four postures including dribbling, passing, catching, and shooting in basketball. The data are collected by four nine‐axis inertial sensors worn on the arm. The time domain and frequency domain features are extracted after the process of smoothing and normalizing. Then 30 eigenvectors are obtained by dimensionality reduction of principal component analysis (PCA), and the support vector machine (SVM) method is used for posture recognition. The experimental results show that after PCA dimensionality reduction, the recognition accuracy of the features as the SVM input is significantly high. Compared with the recognition accuracy of back propagation neural network (BPNN) (85.4%), the average recognition accuracy of SVM is 96%, which verifies the reliability of the method proposed in this study. This research proves the effectiveness of sensor technology in basketball posture recognition, which can provide a reliable guidance for basketball training. © 2020 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.

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Section: Experiments and Resultsmentioning

confidence: 99%

Detection and recognition of human body posture in motion based on sensor technology

Zhao

Chen

2020

IEEJ Transactions Elec Engng

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“…For example, Fatima et al [39] adopted a Support Vector Machine (SVM) based kernel fusion approach for activity recognition and evaluated it on the Milan2009 and Aruba datasets. Fang et al [40] evaluated the application of neural network for activity recognition based on a dataset of two volunteers. Khrishnan and Cook [41] evaluated an online sliding-window based approach for activity recognition on a dataset of three single-occupancy houses.…”

Section: Related Workmentioning

confidence: 99%

Using Sensors to Study Home Activities

Jiang

Pozza

Gunnarsdóttir

et al. 2017

JSAN

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Abstract:Understanding home activities is important in social research to study aspects of home life, e.g., energy-related practices and assisted living arrangements. Common approaches to identifying which activities are being carried out in the home rely on self-reporting, either retrospectively (e.g., interviews, questionnaires, and surveys) or at the time of the activity (e.g., time use diaries). The use of digital sensors may provide an alternative means of observing activities in the home. For example, temperature, humidity and light sensors can report on the physical environment where activities occur, while energy monitors can report information on the electrical devices that are used to assist the activities. One may then be able to infer from the sensor data which activities are taking place. However, it is first necessary to calibrate the sensor data by matching it to activities identified from self-reports. The calibration involves identifying the features in the sensor data that correlate best with the self-reported activities. This in turn requires a good measure of the agreement between the activities detected from sensor-generated data and those recorded in self-reported data. To illustrate how this can be done, we conducted a trial in three single-occupancy households from which we collected data from a suite of sensors and from time use diaries completed by the occupants. For sensor-based activity recognition, we demonstrate the application of Hidden Markov Models with features extracted from mean-shift clustering and change points analysis. A correlation-based feature selection is also applied to reduce the computational cost. A method based on Levenshtein distance for measuring the agreement between the activities detected in the sensor data and that reported by the participants is demonstrated. We then discuss how the features derived from sensor data can be used in activity recognition and how they relate to activities recorded in time use diaries.

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“…Activity recognition in healthcare systems considered as a way to facilitate the work of healthcare in order to treat and care for patients, reduce the workload of medical staff, decrease hospital stays for patients, reduce costs and improve the quality of life for people who need care [1] [2]. Medical experts believe one of the best ways to identify and explore emerging medical conditions is to monitor changes in daily activities, before these conditions become serious [7].…”

Section: Human Activity Recognition In Ambient Assisted Livingmentioning

confidence: 99%

“…Activity recognition in smart homes has made it possible to track occurrences of regular activities in order to monitor healthcare and find changes in activity patterns and lifestyles, so can be a great help in providing automation, security and most importantly remote health monitoring for elderly or people with disabilities [7] [8].…”

Section: Human Activity Recognition In Ambient Assisted Livingmentioning

confidence: 99%

SARF: Smart Activity Recognition Framework in Ambient Assisted Living

Zolfaghari

Keyvanpour

2016

Annals of Computer Science and Information Systems

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Abstract-Human activity recognition in Ambient Assisted Living (AAL) is an important application in health care systems and allows us to track regular activities or even predict these activities in order to monitor healthcare and find changes in patterns and lifestyles. A review of the literature reveals various approaches to discovering and recognizing human activities. The presence of a vast number of activity recognition issues and approaches has made it difficult to make adequate comparisons and accurate assessment. Introducing the five basic components of activity recognition in the smart homes as a famous environment to remote monitoring of patients and independent living for elderly, the present paper proposes SARF framework to classify each of activity recognition approaches and then it is evaluated based on the proposed classification by some proposed measures. Using SARF proposed framework can play an effective role in selecting the appropriate method for human activity recognition in smart homes and beneficial in analysis and evaluation of different methods for various challenges in this field.

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Human activity recognition based on feature selection in smart home using back-propagation algorithm

Cited by 63 publications

References 34 publications

Detection and recognition of human body posture in motion based on sensor technology

Detection and recognition of human body posture in motion based on sensor technology

Using Sensors to Study Home Activities

SARF: Smart Activity Recognition Framework in Ambient Assisted Living

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