Wearable technology has the potential to revolutionize stroke rehabilitation. By objectively assessing and monitoring patients inside and outside clinical environments, wearable technology can enable a more detailed evaluation of the impairment and allow the individualization of rehabilitation therapies. The present review provides an overview of the use of wearable sensors in stroke rehabilitation research, with a particular focus on the upper extremity. Results obtained by current research using a variety of wearable sensors are summarized and used to critically discuss challenges and opportunities in the ongoing effort towards reliable and accessible tools for stroke rehabilitation. Finally, suggestions concerning data acquisition and processing to guide future studies performed by clinicians and engineers alike are provided
Inertial measurement units (IMUs)
are devices that measure acceleration and angular velocity. They are often used in motion tracking applications, where data is collected at high rates to avoid aliasing. IMUs can be used to track human motion or to measure environmental conditions.
Diagnostics
IMUs can be used to assess motor function more objectively. Hester et al. were able to predict hand and arm stages of the Chedoke-McMaster clinical score, while Yu et al. built Brunnstrom stage classifiers, assigning each patient to one of six classes of synergistic movements in affected limbs. The Wolf Motor test, the FMA and the Action Research Arm Test (ARAT), frequently used to assess motor function in clinical settings, have also been automated.
The use of IMUs to assess motor function during the execution of activities of daily life has been shown to be a practical option. Lee and colleagues focused on limb neglect and task execution quality assessment. Limb neglect can be seen by looking at the symmetry (or lack thereof) in sensor readings from the affected and unaffected sides . Zhou et al.used a single, triple-axis accelerometer to track movements of the forearm in a simple manner, but tracking of more complex motion requires either more sensors or alternative data analysis techniques. Harder-to-detect compensatory movements (e.g., of the torso) can also be identified . Besides using IMU modules designed specifically for human movement tracking, interesting possibilities have been explored in every-day-use devices, such as smartphones . Tracking of the whole body has also been achieved using sensor networks in an attempt to objectively evaluate movement quality in daily-life situations , as well as tracking of complex upper-limb movements
Conclusion
Stroke rehabilitation is a complex and iterative process that often involves various assessments, therapies, and training sessions. While this process is essential for many stroke survivors, it can be limited by several factors, including biased predictions of recovery, limited resources, and lack of access to intensive treatment. Wearable sensors have the potential to resolve some of these issues by providing regular and more accurate assessments of motor function. Additionally, short-term rehabilitative training could be prolonged by offering home-based therapies that are designed and monitored remotely by therapists. While there is no perfect solution available on the market at the moment, a combination of inertial measurement units (IMUs) and electromyography (EMG) sensors shows promise as the most promising way to improve stroke rehabilitation.