RNN-Based Personalized Activity Recognition in Multi-person Environment Using RFID

Woo, Sungpil; Byun, Jaewook; Kim, Seonghoon; Nguyen, Hoang Minh; Im, Jungho; Kim, Dae-Young

doi:10.1109/cit.2016.16

Cited by 10 publications

(7 citation statements)

References 10 publications

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“…Clients in the area of behavior detection have lately been exploring customized deep research models for heterogeneity. Woo et al [104] proposed a method for each person to create a model of RNN. Learning Hidden Unit contributions (LHUC) is introduced when [105] is used, with the parameters being trained by limited amounts of data, to incorporate a specific layer with few parameters for each two hidden layers of CNN.…”

Section: Diversitymentioning

confidence: 99%

RETRACTED ARTICLE: Human activity recognition with deep learning: overview, challenges and possibilities

Kumar

Chauhan

2021

CCF Trans. Pervasive Comp. Interact.

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The growing use of sensor tools and the Internet of Things requires sensors to understand the applications. There are major difficulties in realistic situations, though, that can impact the efficiency of the recognition system. Recently, as the utility of deep learning in many fields has been shown, various deep approaches were researched to tackle the challenges of detection and recognition. We present in this review a sample of specialized deep learning approaches for the identification of sensor based human behavior. Next, we present the multi-modal sensory data and include information for the public databases which can be used in different challenge tasks for study. A new taxonomy is then suggested, to organize deep approaches according to challenges. Deep problems and approaches connected to problems are summarized and evaluated to provide an analysis of the ongoing advancement in science. By the conclusion of this research, we are answering unanswered issues and providing perspectives into the future.

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Section: Diversitymentioning

confidence: 99%

RETRACTED ARTICLE: Human activity recognition with deep learning: overview, challenges and possibilities

Kumar

Chauhan

2021

CCF Trans. Pervasive Comp. Interact.

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“… Investigate deep learning classifiers. Deep learning classifiers, like convolutional neural network (CNN), long short-term memory model (LSTM) and gated recurrent units (GRU), have already been widely applied in activity recognition using wearable sensors [ 13 , 87 , 88 , 89 ]. Compared to traditional machine learning algorithms, these classifiers can extract features directly from the raw signals without the need of determining and extracting handcrafted features.…”

Section: Discussionmentioning

confidence: 99%

Recognition of Bathroom Activities in Older Adults Using Wearable Sensors: A Systematic Review and Recommendations

Zhang

D'Haeseleer

Coelho

et al. 2021

Sensors

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This article provides a systematic review of studies on recognising bathroom activities in older adults using wearable sensors. Bathroom activities are an important part of Activities of Daily Living (ADL). The performance on ADL activities is used to predict the ability of older adults to live independently. This paper aims to provide an overview of the studied bathroom activities, the wearable sensors used, different applied methodologies and the tested activity recognition techniques. Six databases were screened up to March 2020, based on four categories of keywords: older adults, activity recognition, bathroom activities and wearable sensors. In total, 4262 unique papers were found, of which only seven met the inclusion criteria. This small number shows that few studies have been conducted in this field. Therefore, in addition, this critical review resulted in several recommendations for future studies. In particular, we recommend to (1) study complex bathroom activities, including multiple movements; (2) recruit participants, especially the target population; (3) conduct both lab and real-life experiments; (4) investigate the optimal number and positions of wearable sensors; (5) choose a suitable annotation method; (6) investigate deep learning models; (7) evaluate the generality of classifiers; and (8) investigate both detection and quality performance of an activity.

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“…Deepika et al (2017) evaluated the long short term memory (LSTM) (Hochreiter and Schmidhuber, 1997) based activity predictor, which outperformed various probabilistic models such as Naive Bayes, HMM and conditional random field. Beyond CNN, for temporal information extraction from inertial sensors, recurrent neural network (Woo et al, 2016), temporal CNN (Nair et al, 2018;Saeed et al, 2018;Garcia et al, 2019) and their variants (Guan and Plötz, 2017) were widely used. Yao et al (2017) used the gated recurrent units (GRUs) to construct HAR.…”

Section: Related Workmentioning

confidence: 99%

Sensor-based human activity recognition using fuzzified deep CNN architecture with λ_max method

Gomathi¹,

Kalaiselvi²,

D³

2022

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Purpose This work aims to develop a novel fuzzy associator rule-based fuzzified deep convolutional neural network (FDCNN) architecture for the classification of smartphone sensor-based human activity recognition. This work mainly focuses on fusing the λmax method for weight initialization, as a data normalization technique, to achieve high accuracy of classification. Design/methodology/approach The major contributions of this work are modeled as FDCNN architecture, which is initially fused with a fuzzy logic based data aggregator. This work significantly focuses on normalizing the University of California, Irvine data set’s statistical parameters before feeding that to convolutional neural network layers. This FDCNN model with λmax method is instrumental in ensuring the faster convergence with improved performance accuracy in sensor based human activity recognition. Impact analysis is carried out to validate the appropriateness of the results with hyper-parameter tuning on the proposed FDCNN model with λmax method. Findings The effectiveness of the proposed FDCNN model with λmax method was outperformed than state-of-the-art models and attained with overall accuracy of 97.89% with overall F1 score as 0.9795. Practical implications The proposed fuzzy associate rule layer (FAL) layer is responsible for feature association based on fuzzy rules and regulates the uncertainty in the sensor data because of signal inferences and noises. Also, the normalized data is subjectively grouped based on the FAL kernel structure weights assigned with the λmax method. Social implications Contributed a novel FDCNN architecture that can support those who are keen in advancing human activity recognition (HAR) recognition. Originality/value A novel FDCNN architecture is implemented with appropriate FAL kernel structures.

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RNN-Based Personalized Activity Recognition in Multi-person Environment Using RFID

Cited by 10 publications

References 10 publications

RETRACTED ARTICLE: Human activity recognition with deep learning: overview, challenges and possibilities

RETRACTED ARTICLE: Human activity recognition with deep learning: overview, challenges and possibilities

Recognition of Bathroom Activities in Older Adults Using Wearable Sensors: A Systematic Review and Recommendations

Sensor-based human activity recognition using fuzzified deep CNN architecture with λ_max method

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RNN-Based Personalized Activity Recognition in Multi-person Environment Using RFID

Cited by 10 publications

References 10 publications

RETRACTED ARTICLE: Human activity recognition with deep learning: overview, challenges and possibilities

RETRACTED ARTICLE: Human activity recognition with deep learning: overview, challenges and possibilities

Recognition of Bathroom Activities in Older Adults Using Wearable Sensors: A Systematic Review and Recommendations

Sensor-based human activity recognition using fuzzified deep CNN architecture with λmax method

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Product

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Sensor-based human activity recognition using fuzzified deep CNN architecture with λ_max method