Abstract
This paper focuses on developing an adaptive heart rate monitoring algorithm for wrist-based rehabilitation systems. Due to the characteristics of the wrist, the heartbeat measurements are unstable. To improve the preprocessing efficiency and perform measurement calibration, a novel joint algorithm incorporating automatic multiscale-based peak detection and fuzzy logic control (AMPD-Fuzzy) is proposed. The monitoring approach consists of two phases: (1) Preprocessing and (2) Detection and Calibration. Phase 1 explores the parameter settings, threshold, and decision rules. Phase 2 applies fuzzy logic control and the Laplacian model to provide signal reshaping. Experimental results show that the proposed algorithm can effectively achieve heart rate monitoring for wearable healthcare devices.
Highlights
The use of photoplethysmography (PPG)-based systems is widespread in clinical applications of heart rate (HR) tracking
The reason for its popularity is partly due to its remarkable properties such as being inexpensive, highly portable, and very convenient to wear by its users
This paper proposes a new algorithm, the automatic multiscale-based peak detection (AMPD)-Fuzzy algorithm, to monitor heart rate for wearable healthcare devices
Summary
The use of photoplethysmography (PPG)-based systems is widespread in clinical applications of heart rate (HR) tracking. The features of wristband-type placement may be utilized to design more convenient and comfortable healthcare systems. Following this concept, we developed a pulmonary rehabilitation (PR) system, evolving from a biosensor module-based approach (Figure 1 (left)) [3] to an arm bag-based way (Figure 1 (right)) [4] to a wrist-based wearable system (Figure 1 (bottom)). This section introduces background information and related work, including the wristband-type PPG-based devices, the sources of measurement inaccuracy, the learning models, and the time-frequency analysis. The wristband-type PPG is considered the most popular and preferred device, these devices have their own limitations [16] related to signal quality. This section introduces the background information, including on the AMPD and Bayesian learning (BL) approaches
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