## 3 Results
### Role of IoT Sensors in Rehabilitation
However, during dynamic movement, the optical signal remains vulnerable to motion artifacts and ambient light interference, which can reduce its accuracy compared to ECG, especially under physically active conditions [20]. Since cardiovascular metrics alone are insufficient for guiding muscular retraining, the attention also shifts from blood flow to muscle activation. While invasive needle EMG offers an unmatched spatial resolution within deep tissues, non-invasive surface EMG (sEMG) utilizes electrodes placed on the skin over target muscles. The surface variant streams real-time coordination and fatigue data to wearable IoT hubs, giving therapists immediate feedback during strengthening routines [21]. The integration of radar technologies into BCG monitoring helps overcome usability challenges encountered with conventional contact devices, and it supports unobtrusive long-term monitoring in both clinical and home environments [22]. When electrical fidelity is required but direct contact is impractical, a capacitive ECG (cECG) offers a viable alternative. By using electrodes separated from the skin by a dielectric layer, a cECG enables long-term monitoring without gels or adhesives. However, variations in the electrode-to-skin distance and body movement may introduce noise, requiring appropriate signal processing to maintain reliability [23]. If physical contact must be avoided, Ultra-Wideband (UWB) radar provides a contactless electromagnetic solution. It emits short pulses across a broad frequency band to detect subtle displacements and spatial positioning through clothing and even walls. This enables the high-resolution, continuous monitoring of posture, movement, and vital signs without requiring any wearable devices. UWB radar supports applications such as fall detection, gait analysis, and postural monitoring, though it relies on precise signal processing due to environmental interference [24; 25]. Its extremely low transmission power and ability to operate unobtrusively within a living environment enhance patient comfort and broaden IoT applications in smart rehabilitation systems, though the complexity of signal processing to mitigate multipath interference remains a technical challenge [24]. Physiological readiness often dictates a patient's potential for recovery. As demonstrated in a study at a heart center, researchers used non-invasive impedance cardiography during routine stress tests to translate beat-to-beat signals into a contractility index. A full curve on this index indicated an eightfold greater chance of a significant VO\({}_{2}\) improvement with exercise-based therapy, prompting clinicians to proceed, while a diminished curve led them to modify the treatment protocol. This unseen sensing transformed the treatment plan without new wires or extra appointments [26]. Movement, traditionally measured by eye or in specialized labs, can now be quantified in everyday settings. While early inertial modules were met with skepticism, contemporary research shows their effectiveness. For example, following a total knee arthroplasty, a two-sensor system on the thigh and shank monitored flexion with less than three degrees of discrepancy compared to a Vicon motion lab during walking and stair climbing [27]. These same sensor pairs achieved an accuracy within eight degrees for the Timed-Up and Go test,allowing patients to demonstrate improvement remotely [28]. The structurally complex shoulder has also been effectively tracked using three sensors (on the sternum, upper arm, and wrist) to provide real-time feedback on a smartphone display. Patients with adhesive capsulitis who used this augmented feedback regained more range of motion and reported less disability compared to those using only printed instructions [29]. In this context, the sensor acts not as a silent witness but as a coach, counting repetitions only when a target angle is achieved.