Wearable-Based Human Activity Recognition Using an IoT Approach

Castro, Diego; Coral, William; Rodríguez, Camilo; Cabra, Jose; Colorado, Julián

doi:10.3390/jsan6040028

Cited by 64 publications

(24 citation statements)

References 34 publications

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“…Similarly to Kekade et al 2018 [2], we also assessed the studies' reporting of research design (Table 2), and the reported participant demography, i.e., number of participants, age, gender and the distribution of healthy participants and patients (see Sections 3.1-3.4 and Table 3). Many studies presented the participant demographics poorly, or not at all [3][4][5][6][7][8][9][10][11][12]. Rather than excluding these from the tables, we indicate missing information with a "-".…”

Section: Qualitative Synthesismentioning

confidence: 99%

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A Systematic Review on the Use of Wearable Body Sensors for Health Monitoring: A Qualitative Synthesis

Kristoffersson

Lindén

2020

Sensors

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The use of wearable body sensors for health monitoring is a quickly growing field with the potential of offering a reliable means for clinical and remote health management. This includes both real-time monitoring and health trend monitoring with the aim to detect/predict health deterioration and also to act as a prevention tool. The aim of this systematic review was to provide a qualitative synthesis of studies using wearable body sensors for health monitoring. The synthesis and analysis have pointed out a number of shortcomings in prior research. Major shortcomings are demonstrated by the majority of the studies adopting an observational research design, too small sample sizes, poorly presented, and/or non-representative participant demographics (i.e., age, gender, patient/healthy). These aspects need to be considered in future research work.

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Section: Qualitative Synthesismentioning

confidence: 99%

“…Our findings are further discussed in Section 4. [5,6] Physical activity recognition 3 ------ [7] Physical activity recognition 65…”

Section: Qualitative Synthesismentioning

confidence: 99%

A Systematic Review on the Use of Wearable Body Sensors for Health Monitoring: A Qualitative Synthesis

Kristoffersson

Lindén

2020

Sensors

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“…Wearable IoT devices have been studied extensively due to their form factor and cost benefits. Researchers have proposed sensor networks, gesture-based control, health monitoring, and activity monitoring as potential applications of IoT devices [16][17][18][19][20][21][22]. Gesture recognition using wearable devices has received significant attention due to its applications in human-computer interaction, gesture-based control, and virtual reality [23][24][25][26].…”

Section: Related Workmentioning

confidence: 99%

Energy per Operation Optimization for Energy-Harvesting Wearable IoT Devices

Park

Bhat

et al. 2020

Sensors

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Wearable internet of things (IoT) devices can enable a variety of biomedical applications, such as gesture recognition, health monitoring, and human activity tracking. Size and weight constraints limit the battery capacity, which leads to frequent charging requirements and user dissatisfaction. Minimizing the energy consumption not only alleviates this problem, but also paves the way for self-powered devices that operate on harvested energy. This paper considers an energy-optimal gesture recognition application that runs on energy-harvesting devices. We first formulate an optimization problem for maximizing the number of recognized gestures when energy budget and accuracy constraints are given. Next, we derive an analytical energy model from the power consumption measurements using a wearable IoT device prototype. Then, we prove that maximizing the number of recognized gestures is equivalent to minimizing the duration of gesture recognition. Finally, we utilize this result to construct an optimization technique that maximizes the number of gestures recognized under the energy budget constraints while satisfying the recognition accuracy requirements. Our extensive evaluations demonstrate that the proposed analytical model is valid for wearable IoT applications, and the optimization approach increases the number of recognized gestures by up to 2.4× compared to a manual optimization.

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“…As in previous studies performed by Banaee et al [37], it was found that SVM, ANN, and tree-based classifiers could be leveraged for healthcare-based uses. Additionally, Casto et al [38], showed that SVM, naïve Bayes, and ANN are potentially much more reliable when it comes to human activity detection. Therefore, for the learning process in this particular study, 5 different machine learning algorithms, namely, random forest, SVM, KNN, naïve Bayes, and ANN, were developed.…”

Section: Machine Learning Algorithm and Evaluation Metricsmentioning

confidence: 99%

The Design of an Automated System for the Analysis of the Activity and Emotional Patterns of Dogs with Wearable Sensors Using Machine Learning

et al. 2019

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The safety and welfare of companion animals such as dogs has become a large challenge in the last few years. To assess the well-being of a dog, it is very important for human beings to understand the activity pattern of the dog, and its emotional behavior. A wearable, sensor-based system is suitable for such ends, as it will be able to monitor the dogs in real-time. However, the question remains unanswered as to what kind of data should be used to detect the activity patterns and emotional patterns, as does another: what should be the location of the sensors for the collection of data and how should we automate the system? Yet these questions remain unanswered, because to date, there is no such system that can address the above-mentioned concerns. The main purpose of this study was (1) to develop a system that can detect the activities and emotions based on the accelerometer and gyroscope signals and (2) to automate the system with robust machine learning techniques for implementing it for real-time situations. Therefore, we propose a system which is based on the data collected from 10 dogs, including nine breeds of various sizes and ages, and both genders. We used machine learning classification techniques for automating the detection and evaluation process. The ground truth fetched for the evaluation process was carried out by taking video recording data in frame per second and the wearable sensors data were collected in parallel with the video recordings. Evaluation of the system was performed using an ANN (artificial neural network), random forest, SVM (support vector machine), KNN (k nearest neighbors), and a naïve Bayes classifier. The robustness of our system was evaluated by taking independent training and validation sets. We achieved an accuracy of 96.58% while detecting the activity and 92.87% while detecting emotional behavior, respectively. This system will help the owners of dogs to track their behavior and emotions in real-life situations for various breeds in different scenarios.

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Wearable-Based Human Activity Recognition Using an IoT Approach

Cited by 64 publications

References 34 publications

A Systematic Review on the Use of Wearable Body Sensors for Health Monitoring: A Qualitative Synthesis

A Systematic Review on the Use of Wearable Body Sensors for Health Monitoring: A Qualitative Synthesis

Energy per Operation Optimization for Energy-Harvesting Wearable IoT Devices

The Design of an Automated System for the Analysis of the Activity and Emotional Patterns of Dogs with Wearable Sensors Using Machine Learning

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