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Mental health disorders, affecting nearly one billion people globally, pose a silent yet pervasive threat to well-being, reducing life expectancy and straining families, workplaces, and healthcare systems. Traditional management tools, clinical interviews, questionnaires, and infrequent check-ins fall short, hampered by subjective biases and their inability to capture the nature of conditions. This chapter explores how wearable technologies, powered by advanced sensors, artificial intelligence (AI), and machine learning (ML), are revolutionizing mental health care by enabling continuous, objective monitoring. Focusing on four approaches – physiological, neurotechnological, contactless, and multimodal we analyze their mechanisms, applications, and transformative potential. These innovations promise proactive care, early intervention, and greater accessibility, yet face challenges. By integrating AI and refining device design, wearable technologies could redefine mental health management, empowering field, though their success hinges on overcoming technical and ethical hurdles. 

Abstract Electromyogram (EMG)-controlled prosthetic hands have advanced significantly during the past two decades. However, most of the currently available prosthetic hands fail to replicate human hand functionality and controllability. To measure the emulation of the human hand by a prosthetic hand, it is important to evaluate the functional characteristics. Moreover, incorporating feedback from end users during clinical testing is crucial for the precise assessment of a prosthetic hand. The work reported in this manuscript unfolds the functional characteristics of an EMG-CoNtrolled PRosthetIC Hand called ENRICH. ENRICH is a real-time EMG controlled prosthetic hand that can grasp objects in 250.8$$ \pm $$1.1 ms, fulfilling the neuromuscular constraint of a human hand. ENRICH is evaluated in comparison to 26 laboratory prototypes and 10 commercial variants of prosthetic hands. The hand was evaluated in terms of size, weight, operation time, weight lifting capacity, finger joint range of motion, control strategy, degrees of freedom, grasp force, and clinical testing. The box and block test and pick and place test showed ENRICH’s functionality and controllability. The functional evaluation reveals that ENRICH has the potential to restore functionality to hand amputees, improving their quality of life. 

Motor impairments caused by stroke significantly affect daily activities and reduce quality of life, highlighting the need for effective rehabilitation strategies. This study presents a novel approach to classifying motor tasks using EEG data from acute stroke patients, focusing on left-hand motor imagery, right-hand motor imagery, and rest states. By using advanced source localization techniques, such as Minimum Norm Estimation (MNE), dipole fitting, and beamforming, integrated with a customized Residual Convolutional Neural Network (ResNetCNN) architecture, we achieved superior spatial pattern recognition in EEG data. Our approach yielded classification accuracies of 91.03% with dipole fitting, 89.07% with MNE, and 87.17% with beamforming, markedly surpassing the 55.57% to 72.21% range of traditional sensor domain methods. These results highlight the efficacy of transitioning from sensor to source domain in capturing precise brain activity. The enhanced accuracy and reliability of our method hold significant potential for advancing brain–computer interfaces (BCIs) in neurorehabilitation. This study emphasizes the importance of using advanced EEG classification techniques to provide clinicians with precise tools for developing individualized therapy plans, potentially leading to substantial improvements in motor function recovery and overall patient outcomes. Future work will focus on integrating these techniques into practical BCI systems and assessing their long-term impact on stroke rehabilitation. 

Stress has been recognized as a pivotal indicator which can lead to severe mental disorders. Persistent exposure to stress will increase the risk for various physical and mental health problems. Early and reliable detection of stress-related status is critical for promoting wellbeing and developing effective interventions. This study attempted multi-type and multi-level stress detection by fusing features extracted from multiple physiological signals including electroencephalography (EEG) and peripheral physiological signals. Eleven healthy individuals participated in validated stress-inducing protocols designed to induce social and mental stress and discriminant multi-level and multi-type stress. A range of machine learning methods were applied and evaluated on physiological signals of various durations. An average accuracy of 98.1% and 97.8% was achieved in identifying stress type and stress level respectively, using 4-s neurophysiological signals. These findings have promising implications for enhancing the precision and practicality of real-time stress monitoring applications. 

Hand gestures are a natural and intuitive form of communication, and integrating this communication method into robotic systems presents significant potential to improve human-robot collaboration. Recent advances in motor neuroscience have focused on replicating human hand movements from synergies also known as movement primitives. Synergies, fundamental building blocks of movement, serve as a potential strategy adapted by the central nervous system to generate and control movements. Identifying how synergies contribute to movement can help in dexterous control of robotics, exoskeletons, prosthetics and extend its applications to rehabilitation. In this paper, 33 static hand gestures were recorded through a single RGB camera and identified in real-time through the MediaPipe framework as participants made various postures with their dominant hand. Assuming an open palm as initial posture, uniform joint angular velocities were obtained from all these gestures. By applying a dimensionality reduction method, kinematic synergies were obtained from these joint angular velocities. Kinematic synergies that explain 98% of variance of movements were utilized to reconstruct new hand gestures using convex optimization. Reconstructed hand gestures and selected kinematic synergies were translated onto a humanoid robot, Mitra, in real-time, as the participants demonstrated various hand gestures. The results showed that by using only few kinematic synergies it is possible to generate various hand gestures, with 95.7% accuracy. Furthermore, utilizing low-dimensional synergies in control of high dimensional end effectors holds promise to enable near-natural human-robot collaboration.