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.

Self-monitoring for spirometry is beneficial to assess the progression of lung disease and the effect of pulmonary rehabilitation. However, home spirometry fails to meet both accuracy and repeatability criteria in a satisfactory manner. The study aimed to propose a pervasive spirometry estimation system with the six-minute walking test (6MWT), where the system with information management, communication protocol, predictive algorithms, and a wrist-worn device, was developed for pulmonary function. A total of 60 subjects suffering from respiratory diseases aged from 25 to 90 were enrolled in the study. Pulmonary function test, walking steps, and physical status were measured before and after performing the 6MWT. The significant variables were extracted to predict per step distance (PSD), forced vital capacity (FVC) and forced expiratory volume in one second (FEV1). These predicted formulas were then implemented in a wrist-worn device of the proposed pervasive estimation system. The predicted models of PSD, and FVC, FEV1 with the 6MWT were created. The estimated difference for PSD was—0.7 ± 9.7 (cm). FVC and FEV1 before performing 6MWT were 0.2 ± 0.6 (L) and 0.1 ± 0.6 (L), respectively, and with a sensitivity (Sn) of 81.8%, a specificity (Sp) of 63.2% for obstructive lung diseases, while FVC and FEV1 after performing the 6MWT were 0.2 ± 0.7 (L) and 0.1 ± 0.6 (L), respectively, with an Sn of 90.9% and an Sp of 63.2% for obstructive lung diseases. Furthermore, the developed wristband prototype of the pulmonary function estimation system was demonstrated to provide effective self-estimation. The proposed system, consisting of hardware, application and algorithms was shown to provide pervasive assessment of the pulmonary function status with the 6MWT. This is a potential tool for self-estimation on FVC and FEV1 for those who cannot conduct home-based spirometry.

Due to the inconvenience of the conventional intravenous drip frame, the piggyback intravenous drip frame is developed to improve the mobility of the patient. However, the current design of the drip frame leads to a lack of balance control and increment of blood returning. To this end, the proposed system aims to solve this problem, and a fuzzy proportionalintegral–derivative control technique is developed to demonstrate the system feasibility. Accordingly, a reliable balanced system can be applied to facilitate patients’ movements and ensure patient safety with compensating the inclination angle of the drip frame such that the reduction of blood returning and the balance control of the piggyback intravenous drip frame can be achieved.