This study reports a novel Hollow Soft Pneumatic Actuator (HOSE), which exhibits 4 degrees of freedom (DOFs). The design consists of a central hollow cylinder surrounded by four twisting symmetric chambers. By virtue of their spiral disposition, each chamber produces a diagonal force along the hollow internal cylinder composed of two components: one parallel to the Z axis and the other one to the plane X-Y. Both top and bottom sections of the actuator are reinforced to avoid deformation, essential for optimal function and dexterity of HOSE. Different movements of the actuator are produced by varying the activation combinations of the 4 chambers. They are constructed from thin walled (0.5 mm) Ecoflex 00-30 super soft silicon rubber, enabling HOSE to perform controlled movements with low pressure not exceeding 35 kPa. HOSE exhibits a maximal extension of 230% of its original length, bends up to i) ±90 0 around X axis, ii) ±115 0 around Y axis, and iii) twists around Z axis with a total range of ±35 0. The paper describes the manufacturing process together with the actuator performance, reporting the range of motion along each DOF related to the internal pressure, volume vs. forces and torques produced along each axis.

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Remote photoplethysmography (rPPG) using camera-based imaging has shown excellent potential recently in vital signs monitoring due to its contactless nature. However, the optimum filter selection for pre-processing rPPG data in signal conditioning is still not straightforward. The best algorithm selection improves the signal-to-noise ratio (SNR) and improves the accuracy of the recognition and classification of vital signs. We recorded more than 300 temporal rPPG recordings, where the noise is mainly not motion-induced. Then, we investigated the best digital filter in pre-processing temporal rPPG data and compare the performances of ten different filters with ten orders each (i.e., total 100 filters). The performances are assessed using a signal quality metric on three levels as the quality of the raw signals was classified under three categories; Q1 being the best Q3 being the worst. The results are presented in SNR scores, which show that the Chebyshev II orders of 2nd, 4th, and 6th perform the best for denoising rPPG signals.