*4.2. Future Work*

Future work on fall detection devices can incorporate SRS for identifying joint kinematics. Moreover, wireless sensor networks, algorithms, and machine learning techniques have been used along with accelerometers and IMUs for fall detection [6,8–10,39] and in the future can also be implemented using SRS. However, adding electromyography (EMG) for fall detection in addition to joint kinematics detection can increase the accuracy of pre-impact fall detection, using both biomechanical and neuromuscular measures. The concept of pre-impact fall detection has been suggested earlier in attempts for early fall detection by using inertial sensors and fall-threshold-detecting algorithms [6,7,22,40]. Pre-impact fall detection research has been successful in detecting fall events at least 70 ms before the impact with the ground [7] and with an average lead time of 700 ms before the impact occurs, with no false alarms [40]. Using IMUs, pre-falls are usually detected due to abnormal or aberrant movement patterns of body segments that occur during falls but do not necessarily occur during regular activities of daily living [7,40]. More recently, a machine learning approach using EMG from the lower extremity has been successful in detecting pre-falls with a lead time of about 775 ms before the fall impact on the ground for forward, backward, and lateral falls [41]. However, as reported in Rucco et al. [22], the use of an accelerometer as a fall detection sensor has been more common due to its low cost and easy application compared with other sensor approaches such as EMG which require more complex sensor positioning, measurement, and analysis. Subsequently more research is warranted with more types of sensors to detect falls more precisely and e fficiently. A combination of wearable stretch sensors, as discussed in this study, and EMG sensors with a machine learning approach can potentially be used for fall detection. The current research team is working on incorporating biomechanical and neuromuscular measures as a wearable solution for detecting falls. Finally, not much research has been conducted on the material properties of the SRS. Future work should also focus on testing the stress–strain properties and attempt to incorporate devices such as nanogenerators that can produce current with no requirements of external power supply that can be a safe and viable option for wearable applications.
