## 3 Results
### Role of IoT Sensors in Rehabilitation
However, the authors also emphasize several engineering challenges, including data heterogeneity, bandwidth limitations, and privacy concerns. Another study explores the growing relevance of wearable IoT devices within structured rehabilitation programs, which the enable real-time tracking of physical activity and heart rates, allowing clinicians to access patient data remotely [40]. In addition to improving motor function, ensuring an autonomous power supply has become a critical requirement for the long-term use of wearable rehabilitation technologies. Study [41] focuses on electromagnetic energy harvesting as a promising solution, while the broader review discusses additional mechanisms such as piezoelectric, triboelectric, and thermoelectric approaches. These devices process data locally on the sensor, eliminating the need for continuous wireless communication, making them especially suitable for long-term recovery scenarios with minimal maintenance requirements. Wireless sensor network simulations have also shown that reinforcement learning algorithms can determine which node sleeps, transmits, or forwards signals, extending battery life without sacrificing coverage [42]. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**IoT Device** & **Primary Parameters** & **Specific Application** & **References** \\ \hline Signal-morphology Impedance & Beat-to-beat stroke volume, cardiac & Predict responsiveness to & [26] \\ Cardiography (thoracic surface electrodes) & output, HR & exercise-based cardiac rehab & [27] \\ MotionSenseTM & triaxial IMU & Knee flexion/extension angles, gait & Objective monitoring after & \\ (accel-gyro-mag) worn on thigh and shank & spatiotemporal metrics & total-knee arthroplasty & [28] \\ Wearable Magnetic-IMU (MIMU) & 3-D knee kinematics (flexion, & Home-based knee rehab, validated & \\ strapped to tibia & varus/valgus, rotation) & vs. optoelectronic system & [29] \\ Wearable single-strap IMU on upper arm & Shoulder ROM, repetition quality & Home exercises for adhesive & [30] \\ On-board IMU in 3D-printed hand & & Bilateral-mode hand exoskeleton & [32] \\ exoskeleton + wireless MCU & Finger joint orientation and motion & control for stroke rehab & [32] \\ Conformal, stretchable, wireless epidermal sEMG array & Forearm muscle activity patterns & Hand-gesture recognition and stroke-hand functional training & [33] \\ EMG sensors & Gesture recognition, forearm muscle signals, mobile robot control & Restoring fine motor skills, & [34] \\ Xbox One Kinect & Depth sensor-based pose & Interactive training for clinical and home use & [35] \\  & estimation, joint position tracking & Stroke recovery, tracking upper limb movements & [36] \\ Wearable EMG smart sensors & Muscle activation signals (EMG & Targeted stroke rehab via a & \\  & data), movement response & closed-loop EMG system with & Functional Electrical Stimulation \\  & & (FES) and VR (Virtual Reality), used & in hospital and home settings \\ Under-mattress & Sleep stages, HR, movement counts & Overnight sleep monitoring during & [37] \\ ballistocardiography/pressure strip & Sleep stages, HR, movement counts & & \\ Wearable accelerometers, & Activity levels, location tracking, & Monitoring elderly patients in & \\ Bluetooth Low-Energy beacons & & subacute rehabilitation, predicting & [38] \\ Microphone array + ambient sensor & Speech acoustics, pronunciation & Elderly speech-rehab learning & \\ nodes & metrics & assistance platform & [39] \\ Wireless Sensor Network (WSN) & Limb kinematics + node energy & Bilateral-mode hand exoskeleton & [30] \\ nodes integrating inertial and & status & control for stroke rehab & [42] \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Overview of wearable and ambient IoT systems in medical rehabilitation.