Green mussel and crab shells are natural sources of CaCO₃, which is widely used as a bioceramic for biomedical applications, although they are commonly disposed of in landfills. The improper disposal of green mussel and crab shells can cause environmental pollution, reducing the quality of life in the community. Many studies have reported the preparation of CaCO₃ from green mussels and crab shells. However, there are limited studies comparing the characteristics, including the crystal phase obtained, weight percentage (%) of crystal, crystal size, crystal system, and elemental composition of CaCO₃ from green mussel shells, crab shells, and commercial CaCO3. The objective of this research was to compare the calcium carbonate properties formed from green mussel (PMS) and crab (PCS) shells to commercial CaCO₃. Green mussel and crab shells were crushed to powder and were calcined at 900 °C for 5 h. Precipitated Calcium Carbonate (PCC) was synthesized from calcined green mussel and crab shells using a solution of 2M HNO₃, NH₄OH, and CO₂ gas. The effect of setting parameters on the synthesized product was analyzed using XRD and SEM-EDX methods. This study shows that the chemical composition of PMS is nearly identical to that of commercial CaCO₃, where no contaminants were identified. In contrast, PCS has N components other than Ca, C, and O. Furthermore, the predominance of the vaterite crystal phases in PMS and PCS, with respective weight percentages of 91.2% and 98.9%, provides a benefit for biomaterial applications. The crystallite sizes of vaterite in PMS, PCS, and calcite in commercial CaCO₃ are 34 nm, 21 nm, and 15 nm, respectively.

Recently, there has been an increase in the number of studies conducted on the process of developing hydroxyapatite (HA) to use in biocomposites. HA can be derived from natural sources such as bovine bone. The HA usage obtained from green mussel shells in biocomposites in this study will be explored. The research goal is to investigate the composition effect of biomaterials derived from polycaprolactone (PCL), polylactic acid (PLA), as well as HA obtained from green mussel shells with a chemical blending method on mechanical properties and degradation rate. First, 80 mL of chloroform solution was utilized to immerse 16 g of the PLA/PCL mixture with the ratios of 85:15 and 60:40 for 30 min. A magnetic stirrer was used to mix the solution for an additional 30 min at a temperature and speed of 50 °C and 300 rpm. Next, the hydroxyapatite (HA) was added in percentages of 5%, 10%, and 15%, as well as 20% of the PLA/PCL mixture’s total weight. It was then stirred for 1 h at 100 rpm at 65 °C to produce a homogeneous mixture of HA and polymer. The biocomposite mixture was then added into a glass mold as per ASTM D790. Following this, biocomposite specimens were tested for their density, biodegradability, and three points of bending in determining the effect of HA and polymer composition on the degradation rate and mechanical properties. According to the findings of this study, increasing the HA and PLA composition yields a rise in the mechanical properties of the biocomposites. However, the biocomposite degradation rate is increasing.

Loss of muscle functions, such as the elbow, can affect the quality of life of a person. This research is aimed at developing an affordable two DOF soft elbow exoskeleton incorporating a dual motor-tendon actuator. The soft elbow exoskeleton can be used to assist two DOF motions of the upper limb, especially elbow and wrist movements. The exoskeleton is developed using fabric for the convenience purpose of the user. The dual motor-tendon actuator subsystem employs two DC motors coupled with lead-to-screw converting motion from angular into linear motion. The output is connected to the upper arm hook on the soft exoskeleton elbow. With this mechanism, the proposed actuator system is able to assist two DOF movements for flexion/extension and pronation/supination motion. Proportional-Integral (PI) control is implemented for controlling the motion. The optimized value of Kp and Ki are 200 and 20, respectively. Based on the test results, there is a slight steady-state error between the first and the second DC motor. When the exoskeleton is worn by a user, it gives more steady-state errors because of the load from the arm weight. The test results demonstrate that the proposed soft exoskeleton elbow can be worn easily and comfortably by a user to assist two DOF for elbow and wrist motion. The resulted range of motion (ROM) for elbow flexion–extension can be varied from 90° to 157°, whereas the maximum of ROM that can be achieved for pronation and supination movements are 19° and 18°, respectively.

Prosthetics and orthotics research, studies, and technologies have been evolving through the years. According to World Health Organization (WHO) data, it is estimated that, globally, 35–40 million people require prosthetics and orthotics usage in daily life. Prosthetics and orthotics demand is increasing due to certain factors. One of the factors is vascular-related disease, which leads to amputation. Prosthetic usage can increase an amputee’s quality of life. Therefore, studies of the ergonomic design of prosthetics are important. The ergonomic factor in design delivers prosthetic products that are comfortable for daily use. One way to incorporate the ergonomic design of prosthetics is by studying the human walking gait. This paper presents a multiclassification of human walking gait based on electromyography (EMG) signals using a machine learning method. An EMG sensor was attached to the bicep femoris longus and gastrocnemius lateral head to acquire the EMG signal. The experiment was conducted by volunteers during normal walking activity at various speeds and the movements were segmented as initial contact, which was labeled as initial gait; loading response to the terminal stance, which was labeled as mid-gait; and pre-swing to terminal swing, which was labeled as final gait. The EMG signal was then characterized using an artificial neural network (ANN) and compared to six training accuracy methods, i.e., the Levenberg–Marquardt backpropagation training algorithm, quasi-Newton training method, Bayesian regulation backpropagation training method, gradient descent backpropagation, gradient descent with adaptive learning rate backpropagation, and one-step secant backpropagation. The machine learning study performed well in the classification of three classes of human walking gait with an overall accuracy (training, testing, and validation) of 96% for Levenberg–Marquardt backpropagation. The gait data will be used to explore the design of lower limb prosthetics in future research.