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Smartwatch Based Activity Recognition Using Active Learning
2017
2017 IEEE/ACM International Conference on Connected Health: Applications, Systems and Engineering Technologies (CHASE)
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This paper describes a smartwatch-based active learning method for activity recognition to identify 5 commonly performed daily activities. ...
From our results, we demonstrate that an interactive learning approach using active learning in smartwatches has significant advantages over smartphones and other devices for activity recognition tasks ...
DISCUSSION In this study, we provided a novel approach for activity recognition problems through the use of smartwatch-based active learning methods. ...
doi:10.1109/chase.2017.115
dblp:conf/chase/ShahmohammadiHK17
fatcat:tinjaish7rgsda3o3syudtls3e
Personalizing Smartwatch Based Activity Recognition Using Transfer Learning
[article]
2019
arXiv
pre-print
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Smartwatches are increasingly being used to recognize human daily life activities. These devices may employ different kind of machine learning (ML) solutions. ...
Results show that this method can significantly improve the user-based accuracy for activity recognition. ...
[8] studied smartwatch based activity recognition using active learning. ...
arXiv:1909.01202v1
fatcat:lwf7wfa6y5govcjgmgnadj47ze
IoT-based Activity Recognition with Machine Learning from Smartwatch
2020
International Journal of Wireless & Mobile Networks
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learning methods for activity recognition. ...
We considered machine learning methods to present the smartwatch as a reliable platform in order to recognize activities, also we considered k-nearest neighbor and decision tree as two popular machine ...
We used Python's Scikit-Learn library for implementing machine learning methods.There are several machine learning methods used for activity recognition in recent years. ...
doi:10.5121/ijwmn.2020.12103
fatcat:kgor34u2izfptppyi5hjnejgpa
Enhanced Hand-Oriented Activity Recognition Based on Smartwatch Sensor Data Using LSTMs
2020
Symmetry
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This work proposes a hybrid deep learning model called CNN-LSTM that employed Long Short-Term Memory (LSTM) networks for activity recognition with the Convolution Neural Network (CNN). ...
Recently, traditional activity recognition techniques have done research in advance by choosing machine learning methods such as artificial neural network, decision tree, support vector machine, and naive ...
The general framework of human activity recognition (HAR) using machine learning approaches.
Figure 2 . 2 Figure 2. The general framework of HAR using deep learning approaches. ...
doi:10.3390/sym12091570
fatcat:4chkzpqh3jbzlfttiqcwzamhja
Outer Product-Based Fusion of Smartwatch Sensor Data for Human Activity Recognition
2022
Zenodo
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To fuse the embedded representations when training the multimodal models, we investigate a concatenation-based and an outer product-based approach. ...
The advent of IoT devices in combination with Human Activity Recognition (HAR) technologies can contribute to battle with sedentariness by continuously monitoring the users' daily activities. ...
Previous works in the literature explored different machine learning techniques, such as hidden Markov models (Ronao and Cho, 2014) , unsupervised learning (Kwon et al., 2014) , and deep learning (Ronao ...
doi:10.5281/zenodo.6610508
fatcat:ynnv3mtobnasdphcraibc2rwfy
Human activity recognition based on machine learning classification of smartwatch accelerometer dataset
Raspoznavanje ljudskih aktivnosti klasifikacijom akcelerometarskih podataka sa pametnih satova pomoću modela mašinskog učenja
2021
FME Transaction
Raspoznavanje ljudskih aktivnosti klasifikacijom akcelerometarskih podataka sa pametnih satova pomoću modela mašinskog učenja
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This paper presents two Machine Learning models that classify time series data given from smartwatch accelerometer of observed subjects. ...
Since the validation and recognition, reached about 81% and 75% respectively, a tendency for improving accuracy as a function of number of participants is considered. ...
INTRODUCTION Monitoring human activities via smart devices, such as smartphones and smartwatches, and processing them by machine-learning-based algorithms, becomes an attractive field of research with ...
doi:10.5937/fme2101225r
fatcat:eumkojbzkbgzzcubdrv3v6rctq
Development and validation of smartwatch-based activity recognition models for rigging crew workers on cable logging operations
2021
PLoS ONE
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These results have demonstrated the feasibility of quantifying forestry work activities using smartwatch-based activity recognition models, a basic step needed to develop real-time safety notifications ...
Random forest machine learning was used to develop predictive models that classified the different cycle elements based on features extracted from the smartwatch acceleration data using 15 sliding window ...
The authors would also like to thank 14 anonymous loggers who supported our research by wearing smartwatches and agreeing to allow us to observe their daily operations. ...
doi:10.1371/journal.pone.0250624
pmid:33979355
pmcid:PMC8115790
fatcat:5dvueaamhrcfjmk3nfwgg4c5cq
Human Activity Recognition with Smartwatch Data by using Mahalanobis Distance-Based Outlier Detection and Ensemble Learning Methods
2023
Academic Platform Journal of Engineering and Smart Systems
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Afterward, the outliers were detected and cleared with a Mahalanobis distance-based approach. ...
With the aim of achieving a better classification performance, machine learning methods were used by strengthening them with ensemble learning methods. ...
[25] investigated the power of the ensemble of classifiers approach for accelerometer-based activity recognition and built a novel activity estimation model grounded on machine learning classification ...
doi:10.21541/apjess.1105362
fatcat:6ekikw4pi5cmreg6tn6em3svsq
Smartphone and Smartwatch-Based Biometrics using Activities of Daily Living
2019
IEEE Access
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Smartphones and smartwatches, which include powerful sensors, provide a readily available platform for implementing and deploying mobile motion-based behavioral biometrics. ...
This suggests that zero-effort continuous biometrics based on normal activities of daily living is feasible, and also demonstrates that certain easy-to-perform activities, such as clapping, may be a viable ...
Such a system would typically employ a twostage approach, where an activity recognition system first recognizes the activity that is being performed, and then the biometric model for that activity is applied ...
doi:10.1109/access.2019.2940729
fatcat:xjtaao4a2zablo2kkxv2z6rpby
Classifying Human Activities with Inertial Sensors: A Machine Learning Approach
[article]
2021
arXiv
pre-print
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We examined and analyzed different Machine Learning and Deep Learning approaches for Human Activity Recognition using inertial sensor data of smartphones. ...
Many of the limitations of computer vision algorithms have been documented in the literature, including research on Deep Neural Network (DNN) and Machine Learning (ML) approaches for activity categorization ...
The study outlines the significance of user behavior and activity recognition (AR) machine learning techniques. ...
arXiv:2111.05333v1
fatcat:r4kzu4fglbdxjou5esxl6zkaq4
A Review of Recent Deep Learning Approaches in Human-Centered Machine Learning
2021
Sensors
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After Deep Learning (DL) regained popularity recently, the Artificial Intelligence (AI) or Machine Learning (ML) field is undergoing rapid growth concerning research and real-world application development ...
These concerns, coupled with the increasing human-AI interactions, have created the emerging field that is Human-Centered Machine Learning (HCML). ...
[116] developed a system for acoustic activity recognition targeting low user burden by using self-supervised learning techniques. ...
doi:10.3390/s21072514
pmid:33916850
pmcid:PMC8038476
fatcat:wvn3av6e2nf7hdxrswfmyyt2te
A Machine Learning Multi-Class Approach for Fall Detection Systems Based on Wearable Sensors with a Study on Sampling Rates Selection
2021
Sensors
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We first applied a preprocessing and a feature extraction stage before using five Machine Learning algorithms, allowing us to compare them. ...
Data come from SisFall, a publicly available dataset containing records of Activities of Daily Living and falls. ...
What is the difference in performance across various types of Machine Learning (ML) algorithms in a FDS? ...
doi:10.3390/s21030938
pmid:33573347
pmcid:PMC7866865
fatcat:7uzloqrsu5fjzgpitpklkgkroi
Classification of Wheelchair Related Shoulder Loading Activities from Wearable Sensor Data: A Machine Learning Approach
2022
Sensors
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It can be concluded that a generalizable algorithm could be trained by a deep learning network to classify wheelchair related SL-ADL from the wearable sensor data. ...
Deep learning networks using bidirectional long short-term memory networks were trained on sensor data (acceleration, gyroscope signals and EMG), using video annotated activities as the target. ...
, and non-propulsive activity [45] [46] [47] 56] , with a variety of machine-learning algorithms. ...
doi:10.3390/s22197404
pmid:36236503
pmcid:PMC9570805
fatcat:rc5rke6z5faufb6rzdbhijcvsa
Detecting Cognitive Impairment Status Using Keystroke Patterns and Physical Activity Data among the Older Adults: A Machine Learning Approach
2021
Journal of Healthcare Engineering
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An advanced ensemble learning algorithm named Gradient Boosting Machine (GBM) is proposed to classify the cognitive severity level (absence, mild, moderate, and severe) based on the Standardised Mini-Mental ...
This paper aims to develop a machine learning model to detect and differentiate cognitive impairment categories like severe, moderate, mild, and normal by analyzing neurophysical and physical data. ...
(a) Data acquisition. (b) Data preprocessing and analyzing features. (c) Machine learning approach. (d) Result analysis. ...
doi:10.1155/2021/1302989
pmid:34966518
pmcid:PMC8712156
fatcat:inpjix3o45hkjmxmouamo5jpde
Stages-Based ECG Signal Analysis from Traditional Signal Processing to Machine Learning Approaches: A Survey
2020
IEEE Access
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We first introduce a stages-based model for ECG signal analysis where a survey of ECG analysis related work is then presented in the form of this stage-based process model. ...
Traditional signal processing methods, machine learning and its subbranches, such as deep learning, are popular techniques for analyzing and classifying the ECG signal and mainly to develop applications ...
processing approaches may not perform as accurate as recent deep and machine learning approaches. ...
doi:10.1109/access.2020.3026968
fatcat:33s5hrmwkvhnzetozcv3hlwkcu
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