Experimentation of Human Activity Recognition by Using Accelerometer Data Based on LSTM

Sa-nguannarm, Phataratah; Elbasani, Ermal; Kim, Bongjae; Kim, Eung-Hee; Kim, Jeong-Dong

doi:10.1007/978-981-15-9309-3_12

Cited by 4 publications

(1 citation statement)

References 13 publications

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“…Then, an LSTM network was used for prediction. In another work, Sa-nguannarm et al [29] proposed a human activity detector based on accelerometer data and an LSTM network. Livieris et al [30] proposed a CNN-LSTM model for gold price time-series forecasting.…”

Section: Related Workmentioning

confidence: 99%

Handwriting Recognition Based on 3D Accelerometer Data by Deep Learning

et al. 2022

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Online handwriting recognition has been the subject of research for many years. Despite that, a limited number of practical applications are currently available. The widespread use of devices such as smartphones, smartwatches, and tablets has not been enough to convince the user to use pen-based interfaces. This implies that more research on the pen interface and recognition methods is still necessary. This paper proposes a handwritten character recognition system based on 3D accelerometer signal processing using Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM). First, a user wearing an MYO armband on the forearm writes a multi-stroke freestyle character on a touchpad by using the finger or a pen. Next, the 3D accelerometer signals generated during the writing process are fed into a CNN, LSTM, or CNN-LSTM network for recognition. The convolutional backbone obtains spatial features in order to feed an LSTM that extracts short-term temporal information. The system was evaluated on a proprietary dataset of 3D accelerometer data collected from multiple users with an armband device, corresponding to handwritten English lowercase letters (a–z) and digits (0–9) in a freestyle. The results show that the proposed system overcomes other systems from the state of the art by 0.53%.

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Section: Related Workmentioning

confidence: 99%

Handwriting Recognition Based on 3D Accelerometer Data by Deep Learning

et al. 2022

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Human Activity Recognition (HAR) Using Deep Learning: Review, Methodologies, Progress and Future Research Directions

Kumar,

Chauhan,

Awasthi

2023

Arch Computat Methods Eng

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Intelligent system for human activity recognition in IoT environment

Khaled

Abu-Elnasr

Elmougy

et al. 2021

Complex Intell. Syst.

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In recent years, the adoption of machine learning has grown steadily in different fields affecting the day-to-day decisions of individuals. This paper presents an intelligent system for recognizing human’s daily activities in a complex IoT environment. An enhanced model of capsule neural network called 1D-HARCapsNe is proposed. This proposed model consists of convolution layer, primary capsule layer, activity capsules flat layer and output layer. It is validated using WISDM dataset collected via smart devices and normalized using the random-SMOTE algorithm to handle the imbalanced behavior of the dataset. The experimental results indicate the potential and strengths of the proposed 1D-HARCapsNet that achieved enhanced performance with an accuracy of 98.67%, precision of 98.66%, recall of 98.67%, and F1-measure of 0.987 which shows major performance enhancement compared to the Conventional CapsNet (accuracy 90.11%, precision 91.88%, recall 89.94%, and F1-measure 0.93).

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Experimentation of Human Activity Recognition by Using Accelerometer Data Based on LSTM

Cited by 4 publications

References 13 publications

Handwriting Recognition Based on 3D Accelerometer Data by Deep Learning

Handwriting Recognition Based on 3D Accelerometer Data by Deep Learning

Human Activity Recognition (HAR) Using Deep Learning: Review, Methodologies, Progress and Future Research Directions

Intelligent system for human activity recognition in IoT environment

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