An investigation of recurrent neural network for daily activity recognition using multi-modal signals

Tamamori, Akira; Hayashi, Tomoki; Toda, Tomoki; Takeda, Kazuya

doi:10.1109/apsipa.2017.8282239

Cited by 10 publications

(14 citation statements)

References 21 publications

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“…Inertial sensors installed in a smartphone have also been used in another study to identify individual physical activities [25]. For more precise recognition, more complex frameworks have been developed that use mutimodal data to train a deep neural network [26]. Other studies used smart home datasets for feature representation along with the application of long short term memory recurrent neural network method [27].…”

Section: Contributions and Paper Overviewmentioning

confidence: 99%

Finding Characteristics of Users in Sensory Information: From Activities to Personality Traits

Lee

Bastos

2020

Sensors

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The main objective of this work was to use information provided by a sensor-based activity recognition system to create a profile comprising user habits and link this information to the personality traits of users. User habits are represented by the sequence and duration of often observed daily life activities. Based on this information, we represented the user trials (sequence of activities) following a numerical method using Fourier series. The duration and sequence of the activities changed the phase and amplitude of the harmonics present in the Fourier representation. Each trial represented in this manner is called a behavioral spectrum. These data and the scores obtained from personality questionnaires were clustered separately and then an association was created between the clusters. The objective was to associate the activity-related features (sensor-based) and personality traits. The experimental results showed that for both young and elderly subjects, there is an association between the user personality traits and the manner in which they perform their activities. Moreover, the results obtained in this work show a promising method of assessing the personality traits of users based on their activities.

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Section: Contributions and Paper Overviewmentioning

confidence: 99%

Finding Characteristics of Users in Sensory Information: From Activities to Personality Traits

Lee

Bastos

2020

Sensors

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“…First, we briefly review the previous studies [6,9]. Next, we mention the unresolved problems to be addressed in this study.…”

Section: I a D I G E S T O F P R E V I O U S S T U D Ymentioning

confidence: 99%

“…Therefore, just simply applying a method for HAR based on deep learning itself is no longer a novel idea, and development of the state-of-the-art deep learning technique for HAR is not our aim. The reason why we still adopt a method based on deep learning in this study is that it outperformed other traditional pattern recognition methods such as k-nearest neighbor, Gaussian mixture model (GMM), a decision tree, and support vector machine (SVM) [6,9]. Additionally, the reason why we adopt the smartphone as a sensor device in this study is that users may not feel an obtrusiveness from it, 2 akira tamamori, et al Overview of our target life-logging system [6]; The system sends the recognition result, the subject's activity to their smartphone.…”

Section: Introductionmentioning

confidence: 99%

“…Before proceeding with our social implementation, it is necessary to address unresolved problems in the previous studies [6,9]: (i) A life-logging system using only a smartphone for recording device has not been developed yet. (ii) Only indoor activities have been utilized for evaluation.…”

Section: Introductionmentioning

confidence: 99%

“…This paper is organized as follows. Section II describes a digest of the previous studies [6,9] and the unresolved problems of these studies. Section III introduces the prototype system towards a realization of our target system.…”

Section: Introductionmentioning

confidence: 99%

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Daily activity recognition based on recurrent neural network using multi-modal signals

Tamamori

Hayashi

Toda

et al. 2018

SIP

Self Cite

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Our aim is to develop a smartphone-based life-logging system. Human activity recognition (HAR) is one of the core techniques to realize it. Recent studies reported the effectiveness of feed-forward neural network (FF-NN) and recurrent neural network (RNN) as a classifier for HAR task. However, there are still unresolved problems in those studies: (1) a life-logging system using only a smartphone for recording device has not been developed, (2) only indoor activities have been utilized for evaluation, (3) insufficient investigations/evaluations of RNN. In this study, we address these unresolved problems as follows: (1) we build a prototype system for life-logging and conduct data recording experiment on this system to include both indoor and outdoor activities. The experimental results of HAR on this new dataset showed that RNN-based classifier was still effective. (2) From the results of a HAR experiment, it was demonstrated that a multi-layered Simple Recurrent Unit with a non-linear transform at the bottom layer and a highway-connection was the most effective. (3) We could grasp the reason for the improvement of RNN from FF-NN by observing the posterior probabilities over test data.

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Content-Aware Network Traffic Prediction Framework for Quality of Service-Aware Dynamic Network Resource Management

Aziz,

Ioannou,

Lestas

et al. 2023

IEEE Access

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Next-generation mobile networks (such as Fifth-Generation (5G), and Sixth-Generation (6G)) are envisioned to undergo an unprecedented transformation from connected things to connected intelligence with more stringent characteristics, i.e., low end-to-end latency, high bandwidth, reliable connectivity, etc. Such networks will significantly increase network traffic in the distribution networks, causing the need for real-time automated decision-making, such as automated network resource allocation. The network resources must be allocated dynamically based on the Quality of Service (QoS) requirements. However, the primary concern is that the distribution networks may get congested soon after allocating network resources. Thus, a QoS-aware prediction framework can be used proactively to predict the future trend of network traffic in each QoS class. In this paper, we propose a framework for predicting the heterogeneous multivariate QoS-aware network traffic to make the best use of network resources dynamically. Specifically, we use a Recurrent Neural Network (RNN) to integrate a Bidirectional Long Short-Term Memory (BLSTM) neural network. The results show that the fusion of RNN-BLSTM can predict the QoS-aware network traffic for over 13 hours with an average accuracy of 97.68%. Moreover, the proposed model is trained and tested over limited data, collected and identified through Deep Packet Inspection (DPI) over an operational network. In addition, we compared the RNN-BLSTM with other prediction algorithms (i.e., LSTM, ARIMA, SVM) in terms of precision, execution time, and energy consumption. Lastly, the proposed framework is used to assign the network resources to each QoS class based on the QoS requirements of that class and its pre-defined priority.INDEX TERMS QoS, heterogeneous multivariate internet traffic, RNN-BLSTM, DPI.

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An investigation of recurrent neural network for daily activity recognition using multi-modal signals

Cited by 10 publications

References 21 publications

Finding Characteristics of Users in Sensory Information: From Activities to Personality Traits

Finding Characteristics of Users in Sensory Information: From Activities to Personality Traits

Daily activity recognition based on recurrent neural network using multi-modal signals

Content-Aware Network Traffic Prediction Framework for Quality of Service-Aware Dynamic Network Resource Management

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