## 3 Results
### Role of IoT Sensors in Rehabilitation
Timely medical intervention is particularly critical during rehabilitation after strokes, surgeries, or neurological treatments, as early adjustments often determine the overall outcome. For instance, one study describes the development of an exoskeleton for individuals with physical impairments, designed for mobility in smart-city environments. It features automatic limb control based on an IoT platform and an AI-powered navigation module that employs an IoT-supported SLAM method [30]. Another innovation is a 3D-printed, bilateral hand exoskeleton made of biodegradable plastic, which operates with five miniature IMUs embedded in a glove on the healthy hand. The mirrored motion data is transmitted via Wi-Fi to servo motors that flex the fingers of the paretic hand in real-time. Costing little, the system transforms the patient's stronger limb into an intuitive joystick for the paretic one, supporting rehabilitation by encouraging symmetric movement and preventing disuse [31]. Adherence is also linked to comfort. To this end, engineers are integrating sensors directly onto the skin. One such device is a flexible copper/polyimide (Cu/PI) serpentine trace printed on breathable silicone, which sits so lightly on the forearm that stroke patients forget they are wearing an eight-channel EMG array [32]. This patch classified seven hand gestures with a 95.81% accuracy and recorded muscle activity during robotic hand training. For direct robot control, six specific gesture classes were used, based on electromyography signals gathered from forearm muscles to operate a mobile robot with a gripping mechanism aimed at restoring fine motor skills. With a high gesture recognition accuracy supported by Gaussian Mixture Networks, this system provides engaging, targeted training suitable for both clinical and home use [33]. Efforts to support stroke recovery have led to the clinical evaluation of depth sensors, like the Xbox One Kinect, aimed at tracking upper limb movements. Although this method shows promise, researchers have noted considerable limitations in tracking joint positions--particularly at the shoulder, elbow, and wrist--calling for improvements that incorporate temporal data and anatomical models to better handle challenges like occlusion [34]. To further enhance stroke rehabilitation, an advanced rehabilitation system has been developed using wearable electromyography smart sensors integrated with functional electrical stimulation and virtual reality, offering a personalized and engaging rehabilitation experience for stroke survivors. By incorporating these sensors in a closed-loop system, the platform supports both hospital- and community-based rehabilitation stages, enhancing accessibility and effectiveness [35]. Sensors have even moved beyond the body, penetrating the spaces where people rest and recover. The thick Emfit QS bed sensor, based on ballistocardiography (BCG), is placed under a rehabilitation mattress in the chest area of a sleeping patient and successfully detects the heartbeat, breathing patterns, and small movements of the torso at night [36]. Harnessing the potential of wearable technology, another approach combines accelerometers and Bluetooth Low-Energy beacons to monitor both the activity levels and location among elderly patients in subacute rehabilitation. This setup enables clinicians to identify subtle indicators, such as the percentage of time spent standing or lying down, offering valuable insights for predicting outcomes like hospital readmission [37]. At the cognitive level, deep learning speech recognizers embedded in virtual reality environments guide elderly people through language exercises, supporting therapy in rural clinics. While the current system lacks speech synthesis and real-time interaction, future research aims to enable a human-machine dialog for more realistic and engaging training scenarios [38]. Nanomaterial-based biosensors hold tremendous potential, especially when integrated with cloud-based AI technologies [39].