Key result
The proposed AMPD-Fuzzy algorithm, incorporating automatic multiscale-based peak detection and fuzzy logic control, effectively achieved heart rate monitoring for wearable healthcare devices.
Why the study?
Heartbeat measurements from wrist-based rehabilitation systems are unstable due to the characteristics of the wrist.
A novel joint algorithm incorporating automatic multiscale-based peak detection and fuzzy logic control improves preprocessing efficiency and measurement calibration for wrist-based heart rate monitoring.
May enhance wearable HR stability in early testing; extends novel algorithm work but leaves clinical adoption open pending larger trials.
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.
No takes yet. Share an insight, caveat, or question.
Kuo et al. (2021) studied this question. AMPD-Fuzzy algorithm was evaluated. The proposed AMPD-Fuzzy algorithm, incorporating automatic multiscale-based peak detection and fuzzy logic control, effectively achieved heart rate monitoring for wearable healthcare devices.
Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context: