Improving Human Activity Recognition using ML and Wearable Sensors-Reference-Cited by-同舟云学术

Improving Human Activity Recognition using ML and Wearable Sensors

Published:2022-05-16 Issue: Volume: Page:
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Container-title:ICC 2022 - IEEE International Conference on Communications
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Author:

Mubibya Gael S.¹,Almhana Jalal¹

Affiliation:

1. Université de Moncton

Funder

Natural Sciences and Engineering Research Council of Canada

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/9837954/9838246/09839267.pdf?arnumber=9839267

Reference21 articles.

1. Challenges in Sensor-based Human Activity Recognition and a Comparative Analysis of Benchmark Datasets: A Review;antar;2019 Joint 8th International Conference on Informatics Electronics Vision (ICIEV) and 2019 3rd International Conference on Imaging Vision Pattern Recognition (icIVPR),2019

2. Comparison of time- and frequency-domain features for movement classification using data from wrist-worn sensors

3. Activity recognition using accelerometer sensor and machine learning classifiers

4. A Machine Learning Approach for Fall Detection and Daily Living Activity Recognition

5. Human activity recognition based on machine learning classification of smartwatch accelerometer dataset

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1. A Study on Hyperparameters Configurations for an Efficient Human Activity Recognition System;Proceedings of the 8th international Workshop on Sensor-Based Activity Recognition and Artificial Intelligence;2023-09-21

2. Efficient Fall Detection using Bidirectional Long Short-Term Memory;2023 International Wireless Communications and Mobile Computing (IWCMC);2023-06-19

3. Real-time Context-aware learning System for IoT Applications;GLOBECOM 2022 - 2022 IEEE Global Communications Conference;2022-12-04