Abstract
Smart healthcare applications depend on data from wearable sensors (WSs) mounted on a patient’s body for frequent monitoring information. Healthcare systems depend on multi-level data for detecting illnesses and consequently delivering correct diagnostic measures. The collection of WS data and integration of that data for diagnostic purposes is a difficult task. This paper proposes an Errorless Data Fusion (EDF) approach to increase posture recognition accuracy. The research is based on a case study in a health organization. With the rise in smart healthcare systems, WS data fusion necessitates careful attention to provide sensitive analysis of the recognized illness. As a result, it is dependent on WS inputs and performs group analysis at a similar rate to improve diagnostic efficiency. Sensor breakdowns, the constant time factor, aggregation, and analysis results all cause errors, resulting in rejected or incorrect suggestions. This paper resolves this problem by using EDF, which is related to patient situational discovery through healthcare surveillance systems. Features of WS data are examined extensively using active and iterative learning to identify errors in specific postures. This technology improves position detection accuracy, analysis duration, and error rate, regardless of user movements. Wearable devices play a critical role in the management and treatment of patients. They can ensure that patients are provided with a unique treatment for their medical needs. This paper discusses the EDF technique for optimizing posture identification accuracy through multi-feature analysis. At first, the patients’ walking patterns are tracked at various time intervals. The characteristics are then evaluated in relation to the stored data using a random forest classifier.
Highlights
A Wearable Sensor (WS) is utilized in the healthcare system to provide technical help as well as remote patient monitoring
The suggested Errorless Data Fusion (EDF) is added to the current techniques twobranch CNN (TCNN) [20], Multi-View Ensemble Algorithm (MVEA) [22], and LSTM-CNN [15]
110 posture patterns were assessed on the front and back from the observing point, with a maximum of 20 features retrieved and a 4 s observation interval
Summary
A Wearable Sensor (WS) is utilized in the healthcare system to provide technical help as well as remote patient monitoring. The collected information is forwarded to a healthcare facility for suggested treatments for the remote user [1]. Data is shared and compared to a predefined dataset. The dataset is made up of a predefined set of medical data and patient information [2]. It is indicated that the patient has a history of communication with healthcare providers and therapy. The WS monitors bodily function-related concerns at predetermined intervals and stores the data [4]. There may be errors and latency when storing the data, problems that must be addressed at the outset. WS data is an important element of safe data transfer [5]
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