## 1 Introduction  
Stroke is among the most frequent and costly causes of new onset disability in adults [1, 2, 3]. About half of all stroke survivors report challenges in upper extremity motor function six months post-stroke, which can include hemiparesis, spasticity, co-contraction, pain, or other limitations which impact quality of life [3, 4, 5, 6]. While ongoing research continues to explore areas of brain plasticity, neural recovery mechanisms, and prognostication of stroke outcomes to maximize recovery, there has been simultaneous advancement in wearable sensor-based technologies that provide additional, noninvasive means of examining neuromuscular pathways and recovery processes following stroke [7, 8, 9, 10]. Several wearable sensor options exist for monitoring recovery in stroke survivors, such as sensing textiles, vital sign or muscle activity electrodes, or inertial measurement units (IMUs), which provide clinicians, researchers, and survivors themselves with valuable information about activity counts, kinematics of the upper or lower extremities during functional tasks or gait, balance responses, and even sleep quality [11, 12, 13]. IMUs have been shown to improve the specificity of monitoring and increase objective understanding of subtle recovery metrics or responsiveness to intervention in both lab and clinic settings; however, challenges remain in implementation due to issues such as battery life, nonstandard or proprietary algorithms across sensor brands, comfort, and signal variation in stroke survivor movements following injury [14,15]. Another key wearable technology is surface electromyography (sEMG). Surface EMG sensors detect and record electrical impulses from muscle tissue that reflect the activation of the corticospinal tract, reflexes, and other neural pathways [9]. Scientists have used sEMG to describe directional patterns of upper extremity muscle recovery, examine co-contraction responses and spasticity, describe changes in latency in muscle response across time, explore firing patterns and interlimb coupling in stroke survivors, and examine the role of sEMG biofeedback to enhance functional rehabilitation outcomes [16, 17, 18, 19]. With the ability to be worn for long periods of time, sEMG can continuously monitor activity and identify changes in motor function not otherwise noted during clinical exams [20, 21, 22]. Current clinical assessments are observational, making it challenging to detect subtle changes and provide valuable feedback for the patient [23,24]. The diagnostic abilities of sEMG may provide an opportunity to overcome such challenges and obtain a detailed assessment of motor control after stroke and throughout recovery, in addition to a better understanding of the effectiveness of therapies [25]. However, sEMG has not been widely adopted in the clinic, despite published standards for use and research indicating that sEMG could be a useful tool in determining long-term prognosis and rehabilitation planning [9, 19, 26]. Further, few studies have evaluated rehabilitation technologies, such as sEMG, during acute and subacute phases of stroke recovery, with the bulk of the literature engaging chronic stroke survivors [27]. Qualitative exploration of perceptions of wearable sensors in clinical settings has also been limited, aside from the previous work of this research group [28, 29]. In the midst of conducting two larger studies, one that incorporated qualitative methods with stroke survivors in the community, and one that focused on quantitative sEMG tracking with a different group of hospitalized participants, the research team had the unique opportunity to perform both sets of research procedures with one participant who was able to be followed through acute hospitalization, subacute recovery in inpatient rehabilitation, and following discharge, due to remaining in the same medical system throughout recovery.