A Novel Smartphone-Based Human Activity Recognition Using Deep Learning in Health care

Soni, Vaibhav; Yadav, Himanshu; Semwal, Vijay Bhaskar; Roy, Bholanath; Choubey, Dilip Kumar; Mallick, Dheeresh K.

doi:10.1007/978-981-19-5868-7_36

Cited by 19 publications

(7 citation statements)

References 31 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The effectiveness and speed of deep learning attracted the attention of researchers. A deep neural network incorporating CNN and Bi-directional LSTM was proposed by Vaibhav Soni et al [16] to recognize the human activity. The suggested model is tested on the UCI-HAR and UCI-WISDM datasets, and both datasets showed a 97.96% and 97.15% accuracy, respectively, for the model's performance.…”

Section: Related Reviewmentioning

confidence: 99%

A Real-Time 3-Dimensional Object Detection Based Human Action Recognition Model

Gupta,

Gill,

Gulia

et al. 2024

IEEE Open J. Comput. Soc.

View full text Add to dashboard Cite

Computer vision technology has made significant strides recently thanks to deep learning and increased computational power. One of the most critical applications of this technology is Human Action Recognition (HAR), which finds use in developing intelligent video surveillance systems and security measures. This paper presents a deep learning-based HAR model that combines a 3D Convolutional neural network (3DCNN), an LSTM multiplicative recurrent network, and Yolov6 for real-time object detection. We have extensively trained our model using various datasets, such as NTU-RGB-D, KITTI, NTU-RGB-D 120, UCF 101, and Fused datasets. Our proposed model outperforms other state-of-the-art (SOTA) methods, including traditional CNN, Yolov6, and CNN with BiLSTM, with impressive accuracies of 98.23%, 97.65%, 98.76%, 95.45%, and 97.65% on different datasets. This paper offers a breakthrough in the field of HAR that has the potential to revolutionize video surveillance systems and security measures.

show abstract

Section: Related Reviewmentioning

confidence: 99%

A Real-Time 3-Dimensional Object Detection Based Human Action Recognition Model

Gupta,

Gill,

Gulia

et al. 2024

IEEE Open J. Comput. Soc.

View full text Add to dashboard Cite

show abstract

“…The work of Soni et al [38], which uses deep learning techniques to recognise human physical activity in wearable and mobile sensor situations, has also garnered a great deal of interest. In this paper, we propose a DNN that combines the strengths of the convolutional neural network (CNN) and the bidirectional long short-term memory (Bi-LSTM).…”

Section: Related Studiesmentioning

confidence: 99%

Smart Health Monitoring Using Deep Learning and Artificial Intelligence

Philip,

Gandhimathi,

Chalichalamala

et al. 2023

RIA

View full text Add to dashboard Cite

The genesis and spread of illnesses are a major concern in today's rapidly developing technological and evolutionary environment. The prevention and management of illnesses using technological means have emerged as one of the most pressing challenges facing the medical community. With today's hectic schedules, it's nearly impossible to stick to a healthy routine. The problems above can be fixed by using a smart health monitoring system. Two of the most rapidly emerging technologies are the Internet of Things (IoT) and artificial intelligence (AI). As more people relocate to urban areas, the idea of a "smart city" has become increasingly commonplace. Increased efficiency, decreased expenses, and a renewed emphasis on improving the quality of care provided to patients are central to the idea of a "smart city." There has to be a thorough familiarity with the various smart city frameworks before the Internet of Things (IoT) and artificial intelligence (AI) can be effectively used for remote healthcare monitoring (RHM) systems. Technologies, gadgets, systems, models, designs, use cases, and applications are all examples of frameworks. The RHM system, based on the Internet of Things, relies heavily on artificial intelligence (AI) and deep learning (DL) to analyse the data it collects. However, DL techniques are widely utilised for making analytical representations, and they are included in CDSS and other kinds of healthcare services. Patients are given personalised recommendations for therapy, lifestyle changes, and care plans by clinical decision support systems after each element is thoroughly analysed. Supporting healthcare applications, this technology may assess activities, etc. In light of this, this paper presents a survey that zeroes in on the best smart city applications for the Internet of Things in health. By analysing the most important monitoring applications across many models using appropriate IoT-based sensors, this research provides a comprehensive assessment of the technologies and systems involved in providing RHM services. Finally, this study makes a contribution to scientific understanding by identifying the primary constraints on this field of study and suggesting directions for further investigation.

show abstract

“…Twenty (20) healthy male participants (age: 26.1 ± 2.86 years) were recruited for this experiment. They are licensed MC drivers with at least 5 years of MC driving experience and are legally allowed to consume alcohol.…”

Section: Participantsmentioning

confidence: 99%

Predicting Intoxication Using Motorcycle and Head Movements of Riders Wearing Alcohol Intoxication Goggles

Seva

Rosario

Peñafiel

et al. 2023

Safety

View full text Add to dashboard Cite

The movement of a motorcycle is one of the critical factors that influences the stability of the ride. It has been established that the gait patterns of drunk and sober people are distinct. However, drunk motorcycle (MC) drivers’ balance has not been investigated as a predictor of intoxication. This paper characterized and used MC and head movements, such as pitch and roll, to predict intoxication while riding. Two separate experiments were conducted to monitor MC and head movement. Male participants were recruited between the ages of 23 and 50 to participate in the study. Participants used alcohol intoxication goggles (AIG) to simulate blood alcohol content (BAC) while driving on a straight path. Placebo goggles were used for control. Results showed that pitch and roll amplitudes of the MC could distinguish drivers wearing placebo and AIGs, as well as the pitch and roll frequency of the head. Deep learning can be used to predict the intoxication of MC riders. The predictive accuracy of the algorithm shows a viable opportunity for the use of movement to monitor drunk riders on the road.

show abstract

A Novel Smartphone-Based Human Activity Recognition Using Deep Learning in Health care

Cited by 19 publications

References 31 publications

A Real-Time 3-Dimensional Object Detection Based Human Action Recognition Model

A Real-Time 3-Dimensional Object Detection Based Human Action Recognition Model

Smart Health Monitoring Using Deep Learning and Artificial Intelligence

Predicting Intoxication Using Motorcycle and Head Movements of Riders Wearing Alcohol Intoxication Goggles

Contact Info

Product

Resources

About