Sparse representation based classification scheme for human activity recognition using smartphones

Jansi, R.; Amutha, R.

doi:10.1007/s11042-018-6662-5

Cited by 21 publications

(6 citation statements)

References 47 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Instead of relying on image data, many researchers have designed wearable sensor technologies for activity monitoring and classification. Jansi et al [19] presented a multi-feature (time and frequency) domain to enhance the classification of eight different human activities from inertial sensors installed in smartphones. Tian et al [20] proposed a two-layer diversity-enhanced multi-classifier recognition method from one triaxle accelerometer to classify four different activities.…”

Section: Related Workmentioning

confidence: 99%

Stochastic Recognition of Physical Activity and Healthcare Using Tri-Axial Inertial Wearable Sensors

2020

View full text Add to dashboard Cite

The classiﬁcation of human activity is becoming one of the most important areas of human health monitoring and physical ﬁtness. With the use of physical activity recognition applications, people suffering from various diseases can be efﬁciently monitored and medical treatment can be administered in a timely fashion. These applications could improve remote services for health care monitoring and delivery. However, the ﬁxed health monitoring devices provided in hospitals limits the subjects’ movement. In particular, our work reports on wearable sensors that provide remote monitoring that periodically checks human health through different postures and activities to give people timely and effective treatment. In this paper, we propose a novel human activity recognition (HAR) system with multiple combined features to monitor human physical movements from continuous sequences via tri-axial inertial sensors. The proposed HAR system ﬁlters 1D signals using a notch ﬁlter that examines the lower/upper cutoff frequencies to calculate the optimal wearable sensor data. Then, it calculates multiple combined features, i.e., statistical features, Mel Frequency Cepstral Coefﬁcients, and Gaussian Mixture Model features. For the classiﬁcation and recognition engine, a Decision Tree classiﬁer optimized by the Binary Grey Wolf Optimization algorithm is proposed. The proposed system is applied and tested on three challenging benchmark datasets to assess the feasibility of the model. The experimental results show that our proposed system attained an exceptional level of performance compared to conventional solutions. We achieved accuracy rates of 88.25%, 93.95%, and 96.83% over MOTIONSENSE, MHEALTH, and the proposed self-annotated IM-AccGyro human-machine dataset, respectively.

show abstract

Section: Related Workmentioning

confidence: 99%

Stochastic Recognition of Physical Activity and Healthcare Using Tri-Axial Inertial Wearable Sensors

2020

View full text Add to dashboard Cite

show abstract

“…Once they were acquired, the raw signals were rarely employed as they are [33,34] but usually some kind of processing was applied to extract a set of informative features. In general, most of extracted features belongs to the time-domain (e.g., mean, standard deviation, minimum value, maximum value, range,…) and the frequency-domain (such as mean and median frequency, spectral entropy, signal power, entropy) [32,35,36]. However, other different variables can be found in literature, such as time-frequency domain variables used in the studies by Eyobu et al [12] and Tian et al [37], or the cepstral features proposed by San-Segundo et al [26] and Vanrell et al [38].…”

Section: Related Workmentioning

confidence: 99%

Comparison of Different Sets of Features for Human Activity Recognition by Wearable Sensors

Rosati

Balestra

Knaflitz

2018

Sensors

View full text Add to dashboard Cite

Human Activity Recognition (HAR) refers to an emerging area of interest for medical, military, and security applications. However, the identification of the features to be used for activity classification and recognition is still an open point. The aim of this study was to compare two different feature sets for HAR. Particularly, we compared a set including time, frequency, and time-frequency domain features widely used in literature (FeatSet_A) with a set of time-domain features derived by considering the physical meaning of the acquired signals (FeatSet_B). The comparison of the two sets were based on the performances obtained using four machine learning classifiers. Sixty-one healthy subjects were asked to perform seven different daily activities wearing a MIMU-based device. Each signal was segmented using a 5-s window and for each window, 222 and 221 variables were extracted for the FeatSet_A and FeatSet_B respectively. Each set was reduced using a Genetic Algorithm (GA) simultaneously performing feature selection and classifier optimization. Our results showed that Support Vector Machine achieved the highest performances using both sets (97.1% and 96.7% for FeatSet_A and FeatSet_B respectively). However, FeatSet_B allows to better understand alterations of the biomechanical behavior in more complex situations, such as when applied to pathological subjects.

show abstract

“…Wearable and ubiquitous sensors like accelerometer and gyroscope can be used to recognize human activities. The emergence of Smartphones has made the activity recognition very simple since sensors like accelerometer and gyroscope are built‐in inside the Smartphone itself 6 …”

Section: Introductionmentioning

confidence: 99%

Elderly Hajj pilgrims activity recognition based on candidate classification technique

Hashim¹,

Amutha

2022

Concurrency and Computation

View full text Add to dashboard Cite

Every year, the rate of elderly people is rising, and monitoring them in mass gatherings like a pilgrimage is a tough task. Millions of Muslims around the world visit Makkah and Madinah for the Hajj pilgrimage. Since 1990, more than 4800 pilgrims have died in the stampede. Aged pilgrims are very much vulnerable to these stampedes. To support them, it is very important to monitor their activities. But only a little is done to monitor the elderly pilgrims. In this article, all the rituals performed in the Hajj pilgrimage have been studied and an activity recognition dataset for elderly pilgrims in line with the rituals of Hajj has been created. A novel classification method called the candidate classification technique (CCT) is proposed to recognize the activities accurately with less response time. Three publicly available activity recognition datasets namely UCI HAR, USC HAD, SKODA, and the proposed elderly hajj pilgrim activity recognition dataset have been used. The accuracy achieved using the proposed classification technique is 98.25%, 96.97%, and 99.29% for the UCI HAR, USC HAD, and SKODA datasets.

show abstract

Sparse representation based classification scheme for human activity recognition using smartphones

Cited by 21 publications

References 47 publications

Stochastic Recognition of Physical Activity and Healthcare Using Tri-Axial Inertial Wearable Sensors

Stochastic Recognition of Physical Activity and Healthcare Using Tri-Axial Inertial Wearable Sensors

Comparison of Different Sets of Features for Human Activity Recognition by Wearable Sensors

Elderly Hajj pilgrims activity recognition based on candidate classification technique

Contact Info

Product

Resources

About