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Myoelectric control of prostheses is a long-established technique, using surface electromyography (sEMG) to detect user intention and perform subsequent mechanical actions. Most machine learning models utilized in control systems are trained using isolated movements that do not reflect the natural movements occurring during daily activities. Moreover, movements are often affected by arm postures, the duration of activities, and personal habits. It is crucial to have a control system for multi-degree-of-freedom (DoF) prosthetic arms that is trained using sEMG data collected from activities of daily living (ADL) tasks. This work focuses on two major functional wrist movements: pronation-supination and dart-throwing movement (DTM), and introduces a new wrist control system that directly maps sEMG signals to the joint velocities of the multi-DoF wrist. Additionally, a specific training strategy (Quick training) is proposed that enables the controller to be applied to new subjects and handle situations where sensors may displace during daily living, muscles can become fatigued, or sensors can become contaminated (e.g., due to sweat). The prosthetic wrist controller is designed based on data from 24 participants and its performance is evaluated using the Root Mean Square Error (RMSE) and Pearson Correlation. The results are found to depend on the characteristics of the tasks. For example, tasks with dart-throwing motion show smaller RSME values (Hammer: 6.68 deg/s and Cup: 7.92 deg/s) compared to tasks with pronation-supination (Bulb: 43.98 deg/s and Screw: 53.64 deg/s). The proposed control technique utilizing Quick training demonstrates a decrease in the average root mean square error (RMSE) value by 35% and an increase in the average Pearson correlation value by 40% across all four ADL tasks. 

An accurate molecular identification of plastic waste is important in increasing the efficacy of automatic plastic sorting in recycling. However, identification of real-world plastic waste, according to their resin identification code, remains challenging due to the lack of techniques that can provide high molecular selectivity. In this study, a standoff photothermal spectroscopy technique, utilizing a microcantilever, was used for acquiring mid-infrared spectra of real-world plastic waste, including those with additives, surface contaminants, and mixed plastics. Analysis of the standoff spectral data, using Convolutional Neural Network (CNN), showed 100% accuracy in selectively identifying real-world plastic waste according to their respective resin identification codes. Standoff photothermal spectroscopy, together with CNN analysis, offers a promising approach for the selective characterization of waste plastics in Material Recovery Facilities (MRFs).