Wearable Internet-of-Things platform for human activity recognition and health care

Iqbal, Asif; Ullah, Farman; Anwar, Hafeez; Rehman, Ata Ur; Shah, Kiran; Baig, Ayesha; Ali, Sajid; Yoo, Sang‐Jo; Kwak, Kyung Sup

doi:10.1177/1550147720911561

Cited by 20 publications

(15 citation statements)

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“…Besides, IoT can help users select the best possible opportunity in any scenario, e.g., cloud resources, management, and decision making [37], thus gaining more attention in academia and industry. For instance, Iqbal et al [38] proposed an IoT wearable sensor-based device to monitor human health. They used several objects: wearable sensors, activity recognition, and smartphones.…”

Section: Related Workmentioning

confidence: 99%

A Multi-Stage Classification Approach for IoT Intrusion Detection Based on Clustering with Oversampling

et al. 2021

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Intrusion detection of IoT-based data is a hot topic and has received a lot of interests from researchers and practitioners since the security of IoT networks is crucial. Both supervised and unsupervised learning methods are used for intrusion detection of IoT networks. This paper proposes an approach of three stages considering a clustering with reduction stage, an oversampling stage, and a classification by a Single Hidden Layer Feed-Forward Neural Network (SLFN) stage. The novelty of the paper resides in the technique of data reduction and data oversampling for generating useful and balanced training data and the hybrid consideration of the unsupervised and supervised methods for detecting the intrusion activities. The experiments were evaluated in terms of accuracy, precision, recall, and G-mean and divided into four steps: measuring the effect of the data reduction with clustering, the evaluation of the framework with basic classifiers, the effect of the oversampling technique, and a comparison with basic classifiers. The results show that SLFN classification technique and the choice of Support Vector Machine and Synthetic Minority Oversampling Technique (SVM-SMOTE) with a ratio of 0.9 and the k value of 3 for k-means++ clustering technique give better results than other values and other classification techniques.

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Section: Related Workmentioning

confidence: 99%

A Multi-Stage Classification Approach for IoT Intrusion Detection Based on Clustering with Oversampling

et al. 2021

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“…The collected data were limited to a window size of three seconds, a set of features was extracted from the window, and a specific label was given to this features set, which was then used for learning purposes to construct a trained model. We extracted several features in both the time and frequency domains, inspired by the literature and [ 48 ]. The details and formulation of some of the features are given below, and the overall features vector processing is shown in Figure 3 .…”

Section: Proposed Wearable-sensors-based Platform For Gesture Recognition Of Autism Spectrum Disorder Childrenmentioning

confidence: 99%

Wearable-Sensors-Based Platform for Gesture Recognition of Autism Spectrum Disorder Children Using Machine Learning Algorithms

Siddiqui

Ullah

Iqbal

et al. 2021

Sensors

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Autistic people face many challenges in various aspects of daily life such as social skills, repetitive behaviors, speech, and verbal communication. They feel hesitant to talk with others. The signs of autism vary from one individual to another, with a range from mild to severe. Autistic children use fewer communicative gestures compared with typically developing children (TD). With time, the parents may learn their gestures and understand what is occurring in their child’s mind. However, it is difficult for other people to understand their gestures. In this paper, we propose a wearable-sensors-based platform to recognize autistic gestures using various classification techniques. The proposed system defines, monitors, and classifies the gestures of the individuals. We propose using wearable sensors that transmit their data using a Bluetooth interface to a data acquisition and classification server. A dataset of 24 gestures is created by 10 autistic children performing each gesture about 10 times. Time- and frequency-domain features are extracted from the sensors’ data, which are classified using k-nearest neighbor (KNN), decision tree, neural network, and random forest models. The main objective of this work is to develop a wearable-sensor-based IoT platform for gesture recognition in children with autism spectrum disorder (ASD). We achieve an accuracy of about 91% with most of the classifiers using dataset cross-validation and leave-one-person-out cross-validation.

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“…Table 2 shows the literature summary of sensors, physical activities, the window size with overlapping and non-overlapping for features extraction, and corresponding extracted features. We adapted most of the features from the works in [ 45 , 46 , 47 , 48 ].…”

Section: Literature Reviewmentioning

confidence: 99%

A Framework for Maternal Physical Activities and Health Monitoring Using Wearable Sensors

Ullah

Iqbal

et al. 2021

Sensors

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We propose a physical activity recognition and monitoring framework based on wearable sensors during maternity. A physical activity can either create or prevent health issues during a given stage of pregnancy depending on its intensity. Thus, it becomes very important to provide continuous feedback by recognizing a physical activity and its intensity. However, such continuous monitoring is very challenging during the whole period of maternity. In addition, maintaining a record of each physical activity, and the time for which it was performed, is also a non-trivial task. We aim at such problems by first recognizing a physical activity via the data of wearable sensors that are put on various parts of body. We avoid the use of smartphones for such task due to the inconvenience caused by wearing it for activities such as “eating”. In our proposed framework, a module worn on body consists of three sensors: a 3-axis accelerometer, 3-axis gyroscope, and temperature sensor. The time-series data from these sensors are sent to a Raspberry-PI via Bluetooth Low Energy (BLE). Various statistical measures (features) of this data are then calculated and represented in features vectors. These feature vectors are then used to train a supervised machine learning algorithm called classifier for the recognition of physical activity from the sensors data. Based on such recognition, the proposed framework sends a message to the care-taker in case of unfavorable situation. We evaluated a number of well-known classifiers on various features developed from overlapped and non-overlapped window size of time-series data. Our novel dataset consists of 10 physical activities performed by 61 subjects at various stages of maternity. On the current dataset, we achieve the highest recognition rate of 89% which is encouraging for a monitoring and feedback system.

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Wearable Internet-of-Things platform for human activity recognition and health care

Cited by 20 publications

References 50 publications

A Multi-Stage Classification Approach for IoT Intrusion Detection Based on Clustering with Oversampling

A Multi-Stage Classification Approach for IoT Intrusion Detection Based on Clustering with Oversampling

Wearable-Sensors-Based Platform for Gesture Recognition of Autism Spectrum Disorder Children Using Machine Learning Algorithms

A Framework for Maternal Physical Activities and Health Monitoring Using Wearable Sensors

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