Affiliations: [a]
Department of EEE, Dr. Mahalingam College of Engineering and Technology, Pollachi, Tamil Nadu, India
| [b]
Department of ECE, Koneru Lakshmaiah Education Foundation, Guntur, AP, India
| [c]
Department of EIE, Velagapudi Ramakrishna Siddhartha Engineering College, Vijayawada, AP, India
| [d]
Department of EEE, Lakireddy Bali Reddy College of Engineering, Mylavaram, India
| [e]
Department of Computer Science, Arasu College of Arts and Science for Women, Karur, India
Correspondence:
[*]
Corresponding author. S. Kanimozhi Suguna, Assistant Professor,
Department of Computer Science, Arasu College of Arts and Science
for Women, Karur, India. E-mail: [email protected].
Abstract: Today’s healthcare sectors are driven and work to rescue patients as soon as possible by giving them the right care and treatment. A healthcare monitoring system works in two ways: by keeping track of the patient’s activities and overall health. For prompt treatment, such as giving the right and suitable medication, administering an injection, and providing additional medical help, nursing supervision is required. Wearable sensors are fixed or connected to the patient’s body and can follow their health. These IoT medical gadgets let clinicians diagnose patients and comprehend the processes from remote. However, the amount of data produced by IoT devices is so large that it cannot be handled manually. A model for automated analysis is required. Convolution Neural Network with Long-Short Term Memory (CNN-LSTM) was therefore suggested in this study as a Hybrid Deep Learning Framework (HDLF) for a Patient Activity Monitoring System (PAMS) that brings all healthcare activities with its classes. To incorporate medical specialists from all over the world and enhance treatment outcomes, the framework offers an advanced model where patient activities, health conditions, medications, and other activities are distributed in the cloud. An effective architecture for Wearable Sensor Network-based Human Action Recognition that combines neural network Simple Recurrent Units (SRUs) and Gated Recurrent Units (GRUs). For assessing the multimodal data input sequence, deep SRUs and a variety of internal memory states is utilized in this research. Furthermore, for addressing the concerns about accuracy oscillations or instability with decreasing gradients, a deep GRUs to store and learn the knowledge is conveyed to the future state. The analysis suggests that CNN-LSTM is then contrasted with some of the currently used algorithms, and it is found that the new system has a 99.53% accuracy rate. The difference between this accuracy result and the current value is at least 4.73%.
Keywords: Sensor network, Body Wearable Sensors, surveillance monitoring, Healthcare Monitoring System (HMS), Physiological Parameter Analyzation
