## 1 Introduction  
The human hand's intrinsic biomechanical complexity and it's overlying multi-level neuronal integration [1] enables precise voluntary movement [2, 3], allows for detailed interaction with the surrounding world [4], plays a crucial role in multisensory integration in the brain [5], and is integral in the execution of many activities of daily living (ADLs) [6]. Loss of hand function is a traumatic consequence of many neurological conditions [7],including SCI and stroke, that significantly affects patient quality of life (QoL) [8; 9], acting as a major barrier to patient independence [10; 11]. Over 15 million people worldwide live with spinal cord injury--most resulting from preventable trauma such as falls, traffic collisions, or violence--and younger age groups are disproportionately affected [12]. Stroke afflicts more than 100 million individuals globally, with over 15 million new cases each year, and remains the third leading cause of death and disability, accounting for some 160 million disability-adjusted life years lost [13; 14]. Both SCI and stroke survivors face elevated risks of secondary complications, increased morbidity and premature mortality, and reduced educational and economic participation. Addressing this dual burden demands integrated strategies for prevention, acute care, rehabilitation, and long-term support to enhance outcomes and societal well-being [15]. Rehabilitation and restoration of hand function after neural damage can be challenging due to the aforementioned biomechanical and neural complexity. Therefore, addressing the challenge requires innovative strategies, among which lies the vision of creating robotic systems that mimic the functionality of human hand physiology [16]. In this context, soft robotics (SR) presents a promising solution by offering improved adaptability and harmony with the hand's dynamic movements [17]. Exercise-based treatments that attempt to stimulate affected muscles' activation while training the patient to perform specific activities have improved the neurologic recovery of the upper extremity in SCI and stroke [18]. The success of these treatments warrants further exploration of their relevance to hand function [19]. In addition, SCI and stroke survivors display a diverse range of phenotypes from complete paralysis to partial weakness and varied degrees of spasticity [20]. Although many patients retain some degree of hand function after SCI or stroke, performing ADLs [21] remains a significant challenge. Achieving complete recovery is often difficult and, in many cases, impossible [22]. To enhance rehabilitation--even in cases of partial recovery--it is essential to employ targeted motor exercises that both quantify patient progress and gauge therapeutic efficacy [23]. Conventional physical therapy is labor-intensive and costly [24], motivating the development of portable, user-friendly orthotic and robotic devices that simplify donning and doffing while remaining affordable [25]. Soft robotic gloves (SRGs) leverage compliant materials and advanced control systems to augment hand function and support sensorimotor retraining [26], yet optimal protocols for their clinical integration remain undefined [27]. We hypothesize that embedding SRGs within neurorehabilitation regimens will enhance motor recovery and drive neuroplastic changes, ultimately improving outcomes for individuals with hand deficits due to SCI or stroke. This study therefore aims to identify and evaluate strategies for effective SRG implementation in therapeutic settings, thereby maximizing their rehabilitative impact and quality-of-life benefits. In this manuscript, we present the design, development, and evaluation experiments integrating ADL features of a wearable prototype device consisting of soft robotic gloves (SRGs). The SRG is tested and evaluated by users with spinal cord injury (SCI) and stroke with impaired hand function. Data from a multiple-sensor array are utilized to drive pneumatic actuation to assist finger movement during grasping tasks.