Comparative analysis of CNN, RNN and LSTM for synthetic grip strength prediction

Syazwan, N. A. A. and Shaharum, Syamimi Mardiah and Faudzi, A. A. M. and W. Samsudin, Wan Syahirah and Sundaraj, Kenneth and Jasni, Farahiyah (2025) Comparative analysis of CNN, RNN and LSTM for synthetic grip strength prediction. In: 8th International Conference on Electrical, Control and Computer Engineering (InECCE 2025), 10th September 2025, Kuantan, Malaysia.

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Abstract

This study assesses the effectiveness of deep learning models, specifically Recurrent Neural Network (RNN), Convolutional Neural Network (CNN), and Long Short-Term Memory (LSTM), in assessing synthetic grip strength data for stress ball and pencil grips. The aim is to determine the optimal model for grip strength classification and tackle issues such as temporal data and class imbalance. Synthetic data was created, preprocessed, and utilized to train the models on Google Colab employing TensorFlow and Keras. Hyperparameters, such as CNN kernel dimensions and RNN/LSTM sequence lengths, were refined. Performance was evaluated by accuracy, precision, F1 scores, and loss metrics. The RNN model surpassed both CNN and LSTM, with an accuracy of 94.99% and a precision of 0.56, particularly excelling in the classification of "Normal" grip strength instances. CNN and LSTM had difficulties with time dependencies and class imbalance, leading to reduced accuracy and increased loss. Although RNN has proven to be the most effective approach, subsequent research should concentrate on mitigating class imbalance and investigating hybrid models to improve performance.

Item Type:	Proceeding Paper (Plenary Papers)
Additional Information:	8790/124565
Uncontrolled Keywords:	Deep learning, Recurrent Neural Network (RNN), synthetic data, Grip Strength Analysis
Subjects:	T Technology > TA Engineering (General). Civil engineering (General)
Kulliyyahs/Centres/Divisions/Institutes (Can select more than one option. Press CONTROL button):	Kulliyyah of Engineering > Department of Mechatronics Engineering Kulliyyah of Engineering
Depositing User:	Dr Farahiyah Jasni
Date Deposited:	26 Nov 2025 09:09
Last Modified:	26 Nov 2025 11:09
Queue Number:	2025-11-Q122
URI:	http://irep.iium.edu.my/id/eprint/124565

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