An ANFIS-based Human Activity Recognition using IMU sensor Fusion

Ajani, Oladayo S.; El-Hussieny, Haitham

doi:10.1109/niles.2019.8909289

Cited by 7 publications

(3 citation statements)

References 8 publications

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“…Each IMU sensor consists of one accelerometer sensor and one gyroscope sensor. Based on the existing research works [ 3 , 25 , 26 ], we used only 3-axis gyroscope signals of each IMU sensor for monitoring the movement of teres and latissimus muscles while the EMG signals to monitor the movement of bicep muscles. The two gyroscope sensors are located between the upper back shoulder blades and the middle back lumbar support of the smart fitness suite, as displayed in Figure 4 .…”

Section: Proposed Methodologymentioning

confidence: 99%

A Portable Smart Fitness Suite for Real-Time Exercise Monitoring and Posture Correction

Hannan

Shafiq

Hussain

et al. 2021

Sensors

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Fitness and sport have drawn significant attention in wearable and persuasive computing. Physical activities are worthwhile for health, well-being, improved fitness levels, lower mental pressure and tension levels. Nonetheless, during high-power and commanding workouts, there is a high likelihood that physical fitness is seriously influenced. Jarring motions and improper posture during workouts can lead to temporary or permanent disability. With the advent of technological advances, activity acknowledgment dependent on wearable sensors has pulled in countless studies. Still, a fully portable smart fitness suite is not industrialized, which is the central need of today’s time, especially in the Covid-19 pandemic. Considering the effectiveness of this issue, we proposed a fully portable smart fitness suite for the household to carry on their routine exercises without any physical gym trainer and gym environment. The proposed system considers two exercises, i.e., T-bar and bicep curl with the assistance of the virtual real-time android application, acting as a gym trainer overall. The proposed fitness suite is embedded with a gyroscope and EMG sensory modules for performing the above two exercises. It provided alerts on unhealthy, wrong posture movements over an android app and is guided to the best possible posture based on sensor values. The KNN classification model is used for prediction and guidance for the user while performing a particular exercise with the help of an android application-based virtual gym trainer through a text-to-speech module. The proposed system attained 89% accuracy, which is quite effective with portability and a virtually assisted gym trainer feature.

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Section: Proposed Methodologymentioning

confidence: 99%

A Portable Smart Fitness Suite for Real-Time Exercise Monitoring and Posture Correction

Hannan

Shafiq

Hussain

et al. 2021

Sensors

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“…The HAR system developed by the authors [ 40 , 41 , 42 ] shows that due to its limited ability to capture information, a single IMU can only recognize continuous actions and cannot identify actions containing switching processes, despite the good feature processing and classification methods used. The authors in [ 40 ] classify the body actions into four categories, considering continuous actions with a classification accuracy 98.88%. However, the transition process (i.e., from the current action to the new action) is not considered.…”

Section: Related Workmentioning

confidence: 99%

Hybrid Learning Models for IMU-Based HAR with Feature Analysis and Data Correction

Tseng,

Wen

2023

Sensors

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This paper proposes a novel approach to tackle the human activity recognition (HAR) problem. Four classes of body movement datasets, namely stand-up, sit-down, run, and walk, are applied to perform HAR. Instead of using vision-based solutions, we address the HAR challenge by implementing a real-time HAR system architecture with a wearable inertial measurement unit (IMU) sensor, which aims to achieve networked sensing and data sampling of human activity, data pre-processing and feature analysis, data generation and correction, and activity classification using hybrid learning models. Referring to the experimental results, the proposed system selects the pre-trained eXtreme Gradient Boosting (XGBoost) model and the Convolutional Variational Autoencoder (CVAE) model as the classifier and generator, respectively, with 96.03% classification accuracy.

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“…While it refrains from specifically addressing GPS-IMU incorporation, it offers details on the application of ANFIS for IMU data error estimation, and the outcome implies that the ANFIS could substantially enhance the accuracy of inertial navigation positioning, which is important for vehicle inertial navigation in intricate and covert settings [25]. Research by [26] presents an ANFIS-based approach to categorizing everyday life events using data collected by IMU sensors. Although it concentrates on identifying activities rather than GPS-IMU data correction, it still exhibits the usage of ANFIS in sensor fusion with a total accuracy of 98.88%.…”

Section: Introductionmentioning

confidence: 99%

Adaptive Neuro-Fuzzy Inference System-Based GPS-IMU Data Correction for Capacitive Resistivity Underground Imaging with Towed Vehicle System

Dadios,

Jahara Baun,

Louie Enriquez

et al. 2023

Advances in Fuzzy Logic Systems

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This study proposes the utilization of an Adaptive Neuro-Fuzzy Inference System (ANFIS) to correct the latitude and longitude of Global Positioning System (GPS) used in locating towed vehicle system for underground imaging. The input used was the collected data from a developed Real-time Kinematic Global Positioning System sensor integrated with Inertial Measurement Unit. Different ANFIS models were developed and evaluated. For latitude correction, ANFIS model with hybrid optimization trained at 300 epochs was chosen, whereas for longitude correction, ANFIS model with hybrid optimization trained at 100 epochs was selected. Both models achieved the lowest Mean Squared Error (MSE), the highest Coefficient of Determination (R2), and lowest Mean Absolute Error (MAE). Moreover, selected best ANFIS models were compared to Long Short-Term Memory (LSTM) and Extreme Learning Machine (ELM) models, but the results showed that the ANFIS models have superior performances. The selected ANFIS models were verified by testing on the collected actual dataset and the visualized map demonstrated that the corrected GPS latitude and longitude have significantly reduced error, indicating that the fuzzy system with neural network capabilities is a cost-effective and convenient method for error reduction in vehicle localization making it applicable to be integrated for capacitive resistivity underground imaging systems.

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An ANFIS-based Human Activity Recognition using IMU sensor Fusion

Cited by 7 publications

References 8 publications

A Portable Smart Fitness Suite for Real-Time Exercise Monitoring and Posture Correction

A Portable Smart Fitness Suite for Real-Time Exercise Monitoring and Posture Correction

Hybrid Learning Models for IMU-Based HAR with Feature Analysis and Data Correction

Adaptive Neuro-Fuzzy Inference System-Based GPS-IMU Data Correction for Capacitive Resistivity Underground Imaging with Towed Vehicle System

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