An efficient and secure wearable sensor based remote healthcare monitoring system using adaptive dilated transformer Bi‐LSTM with gated recurrent unit

Abstract

AbstractInnovative research works in the healthcare sector keep on advancing every day. As the “Internet of Things (IoT)” keeps on evolving, the application of IoT in the medical field is prominent these days. Utilizing IoT devices, alert messages can be sent directly to medical professionals in case of an emergency. So, monitoring the health condition of an individual using IoT technology has become a popular and beneficial method in today's contemporary medical field. With the help of mobile IoT medical equipment, the technology of smart Healthcare Monitoring System (HMS) is proliferating. By utilizing deep learning and IoT technology, the medical diagnosis system has evolved from direct face‐to‐face visits to the hospital to remote telemedicine method. Most of the data generated by the IoT wearable sensors are highly correlated and may consist of outliers. The extraction of the essential attributes from these data is a complicated task. So, “deep learning and machine learning” techniques are adapted to determine the most relevant and appropriate feature required for efficient diagnosis from the unstructured data produced by the IoT devices and thus help in minimizing the redundancy of unnecessary data. Fusing deep learning methods with healthcare IoT made only the essential details to be available for diagnosis. Therefore, a deep learning‐oriented IoT‐based HMS is executed in this work. With the support of several wearable healthcare devices, the required data are acquired. The encryption of the data acquired from standard sources using Optimal Key‐based Advanced Encryption Standard (OK‐AES) is carried out next to assure the security of the sensitive medical data. The keys for AES encryption are optimally chosen with the aid of the Enhanced Heap‐Based Optimizer Algorithm (EHBOA). The encrypted data is transferred to the “cloud platform” for data storage. Once there is a need for the data, then the encrypted data is initially downloaded from the cloud platform. Then using the same AES scheme, the decryption of the data to attain the original data is carried out. From the retrieved data, the extraction of the crucial attributes is carried out. The extracted features are chosen in an optimized manner and are concatenated with the tuned weights to form the weighted feature matrix. This formulated weighted feature matrix is provided as input to the “Adaptive Dilated Transformer Bidirectional Long Short‐Term Memory (Bi‐LSTM) with Gated Recurrent Unit (GRU) (ADTBi‐LSTM‐GRU) model.” The variables in the ADTBG are optimized using the EHBOA for providing an accurate classification outcome. The classified disease outcome is obtained from the deployed ADTBi‐LSTM‐GRU model. Simulations are done to verify the efficiency and reliability of the implemented deep learning and IoT‐based HMS.