Stroke is the third leading cause of disability worldwide [1].
At least 50% of stroke survivors suffer from upper limb impairments
that limit engagement in activities of daily living (ADLs) and reduce
quality of life [2, 3]. There is a consensus that effective post-stroke upper limb rehabilitation benefits from high-dose repetitive-task training [4, 5].
Nonetheless, ensuring the delivery of sufficient training doses remains
challenging, especially during the acute to subacute phases [6,7,8,9,10].
Additionally, it’s essential to emphasize the significant impact of
treatment timing on post-stroke motor recovery. Research has shown that
the optimal rehabilitation period occurs within the first 60 to 90 days
following a stroke [11].
Furthermore, clinical trials on human subjects have consistently
revealed that individuals receiving early intervention exhibit
significantly better motor recovery outcomes compared to those who
received delayed intervention [12,13,14]. Building on this understanding, recent studies [15, 16]
also highlighted the potential of rehabilitation technologies in
alleviating the burden of intensive and repetitive upper limb exercises
for therapists. These technologies, facilitating high-dose upper limb
rehabilitation during the early stages of stroke, even when the upper
limb is still significantly weakened and unable to generate overt
movement, may serve as a beneficial supplement to conventional treatment
methods.
To address the unique challenges posed by post-stroke
rehabilitation, various sensor-based technological solutions have been
explored, such as the Leap Motion [17], Kinect [18, 19], and Myo armband [18, 20, 21]. While demonstrating promise in gesture recognition [22] and hand therapy with serious games [23, 24], the Leap Motion generally demands precise hand positioning and controlled lighting conditions [23, 25].
It is important to emphasize that the former may present practical
challenges, particularly for individuals with highly impaired upper
limbs, as they might encounter difficulty holding their shoulder and
elbow in the necessary position for an extended duration. Kinect-based
programs have also shown promise in upper limb rehabilitation [26,27,28].
However, they also exhibit technical limitations, including the
complexity of use and dependence on the therapist’s assistance [19], along with challenges associated with occlusion [29], and reduced reliability for small movement amplitudes [29].
In
this context, surface electromyography (sEMG) emerges as a versatile
and promising avenue for post-stroke rehabilitation, alleviating users
from strict positioning constraints and lighting considerations.
Specifically, the integration of sEMG with the Myo armband introduces a
wireless and highly portable dimension to upper limb rehabilitation.
Furthermore, sEMG possesses the unique ability to detect movement
intention in cases of upper limb paresis [30, 31].
Such capability is vital for stroke patients with limited active
movement. The effectiveness of sEMG biofeedback has been demonstrated in
prior studies, encompassing gait training [32, 33] and upper limb exercises, with notable advantages such as mitigating co-contraction patterns [34], achieving enhanced functional recovery outcomes compared to conventional therapy [35, 36], and receiving positive usability feedback from end-users [37].
The potential use of the Myo armband with gamified applications for
upper limb rehabilitation is highlighted by recent studies in multiple
sclerosis and stroke patients for hand/wrist rehabilitation [21, 38].
Furthermore, it’s worth noting that although the Myo armband is no
longer on the market, alternatives continue to be available.
The usability of the Myo armband coupled with serious games has also been tested on both healthy participants [20] and health professionals [38].
However, its utility as a user-friendly tool in clinical settings for
both stroke patients and healthcare professionals has not been
thoroughly investigated. Furthermore, given the heterogeneity in the
design of serious games, the generalizability of usability across
different game types may be limited. To effectively pinpoint and address
usability challenges, a rigorous and iterative evaluation process is
required [39,40,41].
Previous studies have shown that inclusive participation of both
patients and therapists has played a pivotal role in shaping the
development of wearable exoskeletons [42, 43] and interactive game-based virtual reality systems [44].
Acknowledging
this gap, our present research seeks to offer insights into the
usability of the Myo armband integrated with a training platform
specifically designed for post-stroke rehabilitation. In this study, we
introduce ‘MyoGuide,’ a mobile platform with a serious game harnessing
the benefits of sEMG-guided biofeedback training. Additionally, it
incorporates a calibration feature to address the diverse nature of
impairments observed among stroke patients. Our focus is on wrist
extension training, a pivotal aspect for both activities of daily living
(ADLs) [45, 46] and hand grasping actions [47, 48]. Additionally, wrist extension ability has been highlighted as a potential indicator of upper limb functional recovery [49].
In
this study, our primary objective was to integrate usability
assessments into the initial stages of MyoGuide’s development, targeting
both stroke survivors and therapists. From stroke survivors, we aimed
to gain insights into the overall usability of the training platform.
From therapists, our goal was to assess whether MyoGuide serves as a
tool that can be used in clinical settings. The overarching aim is to
validate MyoGuide’s suitability for integration into clinical settings,
particularly addressing challenges related to intensive and repetitive
upper limb exercises.
More at link.
; Li Guo3 
