Acoustic sensor based activity recognition using ensemble of one-class classifiers

Tripathi, Achyut Mani; Baruah, Diganta; Baruah, Rashmi Dutta

doi:10.1109/eais.2015.7368798

Cited by 7 publications

(4 citation statements)

References 21 publications

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“…Then they forwarded those features to a vanilla CNN for the classification of walking, sitting, falling, and standing. Tripathi et al [41] used acoustic sensors to detect the locomotion activities of humans at bus stops and parks and generated perceptual features. They used an ensemble of one-class classifiers that were based on fuzzy rules.…”

Section: Rs-hlar Using Proximity Sensorsmentioning

confidence: 99%

Smartphone Sensor-Based Human Locomotion Surveillance System Using Multilayer Perceptron

et al. 2022

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Applied sensing technology has made it possible for human beings to experience a revolutionary aspect of the science and technology world. Along with many other fields in which this technology is working wonders, human locomotion activity recognition, which finds applications in healthcare, smart homes, life-logging, and many other fields, is also proving to be a landmark. The purpose of this study is to develop a novel model that can robustly handle divergent data that are acquired remotely from various sensors and make an accurate classification of human locomotion activities. The biggest support for remotely sensed human locomotion activity recognition (RS-HLAR) is provided by modern smartphones. In this paper, we propose a robust model for an RS-HLAR that is trained and tested on remotely extracted data from smartphone-embedded sensors. Initially, the system denoises the input data and then performs windowing and segmentation. Then, this preprocessed data goes to the feature extraction module where Parseval’s energy, skewness, kurtosis, Shannon entropy, and statistical features from the time domain and the frequency domain are extracted from it. Advancing further, by using Luca-measure fuzzy entropy (LFE) and Lukasiewicz similarity measure (LS)–based feature selection, the system drops the least-informative features and shrinks the feature set by 25%. In the next step, the Yeo–Johnson power transform is applied, which is a maximum-likelihood-based feature optimization algorithm. The optimized feature set is then forwarded to the multilayer perceptron (MLP) classifier that performs the classification. MLP uses the cross-validation technique for training and testing to generate reliable results. We designed our system while experimenting on three benchmark datasets namely, MobiAct_v2.0, Real-World HAR, and Real-Life HAR. The proposed model outperforms the existing state-of-the-art models by scoring a mean accuracy of 84.49% on MobiAct_v2.0, 94.16% on Real-World HAR, and 95.89% on Real-Life HAR. Although our system can accurately differentiate among similar activities, excessive noise in data and complex activities have shown an inverse effect on its performance.

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Section: Rs-hlar Using Proximity Sensorsmentioning

confidence: 99%

Smartphone Sensor-Based Human Locomotion Surveillance System Using Multilayer Perceptron

et al. 2022

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“…The evaluation showed the impact of decision-level fusion for human activity classification as the authors achieved 98% accuracy with an average of probability fusion method. Tripathi et al [53] investigated the fuzzy decision rule algorithm that uses simple combination rule for adaptive based human activity identification. The authors formulated new classifier as a batch of new activity details.…”

Section: Multiple Classifier Systemsmentioning

confidence: 99%

Multi-sensor fusion based on multiple classifier systems for human activity identification

Nweke

Wah

Mujtaba

et al. 2019

Hum. Cent. Comput. Inf. Sci.

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Multimodal sensors in healthcare applications have been increasingly researched because it facilitates automatic and comprehensive monitoring of human behaviors, high-intensity sports management, energy expenditure estimation, and postural detection. Recent studies have shown the importance of multi-sensor fusion to achieve robustness, high-performance generalization, provide diversity and tackle challenging issue that maybe difficult with single sensor values. The aim of this study is to propose an innovative multi-sensor fusion framework to improve human activity detection performances and reduce misrecognition rate. The study proposes a multiview ensemble algorithm to integrate predicted values of different motion sensors. To this end, computationally efficient classification algorithms such as decision tree, logistic regression and k-Nearest Neighbors were used to implement diverse, flexible and dynamic human activity detection systems. To provide compact feature vector representation, we studied hybrid bio-inspired evolutionary search algorithm and correlation-based feature selection method and evaluate their impact on extracted feature vectors from individual sensor modality. Furthermore, we utilized Synthetic Over-sampling minority Techniques (SMOTE) algorithm to reduce the impact of class imbalance and improve performance results. With the above methods, this paper provides unified framework to resolve major challenges in human activity identification. The performance results obtained using two publicly available datasets showed significant improvement over baseline methods in the detection of specific activity details and reduced error rate. The performance results of our evaluation showed 3% to 24% improvement in accuracy, recall, precision, F-measure and detection ability (AUC) compared to single sensors and feature-level fusion. The benefit of the proposed multisensor fusion is the ability to utilize distinct feature characteristics of individual sensor and multiple classifier systems to improve recognition accuracy. In addition, the study suggests a promising potential of hybrid feature selection approach, diversity-based multiple classifier systems to improve mobile and wearable sensor-based human activity detection and health monitoring system.

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“…Giannakopoulos and Siantikos [11] developed an activity recognition system for elderly monitoring that uses non-verbal information from the audio channel. Other research on acoustic-based activity recognition followed similar approaches [15,29]. This prior work, however, focuses on audio signals and sounds from sensors, and has not used textual content from speech to recognize activities.…”

Section: Related Workmentioning

confidence: 99%

An Analysis of Speech as a Modality for Activity Recognition during Complex Medical Teamwork

Jagannath

Sarcevic

Marsic

2018

Proceedings of the 12th EAI International Conference on Pervasive Computing Technologies for Healthcare

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We analyzed the nature of verbal communication among team members in a dynamic medical setting of trauma resuscitation to inform the design of a speech-based automatic activity recognition system. Using speech transcripts from 20 resuscitations, we identified common keywords and speech patterns for different resuscitation activities. Based on these patterns, we developed narrative schemas (speech “workflow” models) for five most frequently performed activities and applied linguistic models to represent relationships between sentences. We evaluated the narrative schemas with 17 new cases, finding that all five schemas adequately represented speech during activities and could serve as a basis for speech-based activity recognition. We also identified similarities between narrative schemas of different activities. We conclude with design implications and challenges associated with speech-based activity recognition in complex medical processes.

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Acoustic sensor based activity recognition using ensemble of one-class classifiers

Cited by 7 publications

References 21 publications

Smartphone Sensor-Based Human Locomotion Surveillance System Using Multilayer Perceptron

Smartphone Sensor-Based Human Locomotion Surveillance System Using Multilayer Perceptron

Multi-sensor fusion based on multiple classifier systems for human activity identification

An Analysis of Speech as a Modality for Activity Recognition during Complex Medical Teamwork

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