Work-related muscle disorders are a main cause of missed work, globally, and arecostly for public health systems. However, development of musculoskeletal tissue diagnostics is lagging compared to other tissues and organs. Myofascial trigger points (MTP) are unique muscle tissue phenomenon that are challenging to address due to a lack of objective assessment methodology. This study seeks to meet this need by devising a non-invasive, objective methodology for evaluating musculoskeletal tissue following intervention or physical provocation, specific to the anterior forearm region. In Aim 1, current literature on MTP pathophysiology informs a multi-modal assessment approach, including: 1) pain pressure threshold (PPT), 2) power Doppler (PD) ultrasound, 3) strain elastography (SE), and 4) surface electromyography (sEMG). In Aim 2, controlled ultrasound image acquisition and standardization techniques are developed for imaging muscle tissue with PD (Aim 2a) and SE (Aim 2b) . These techniques improved differentiability of vascularity and compliance estimation after physical provocation or intervention. In Aim 3, the multi-modal approach is implemented in a human pilot study (n=34) investigating MTP response to osteopathic manipulative treatment, compared to rest and light exercise. Positive trends and significant changes are detected after OMT and rest. PPT significantly increased after OMT (p = 0.021). Tissue compliance significantly increase after rest (p ≪ 0.0001) and after OMT( p = 0.002). Principal component analysis finds 9 of 13 outcome measures to be salient features of MTP treatment effect. The data suggests high and low responders, yielding insights for improved patient screening and study design for future work. With further optimization and development, this method may be applied to a broad array of clinical scenarios for musculoskeletal tissue evaluation directed towards amelioration of neuromuscular symptoms.

Stroke accounts for high rates of mortality and disability in the United States. It levies great economic burden on the affected subjects, their family and the society at large. Motor impairments after stroke mainly manifest themselves as hemiplegia or hemiparesis in the upper and lower limbs. Motor recovery is highly variable but can be enhanced through motor rehabilitation with sufficient movement repetition and intensity. Cost effective assistive devices that can augment therapy by increasing movement repetition both at home and in the clinic may facilitate recovery. This thesis aims to develop a Smart Glove that can enhance motor recovery by providing feedback to both the therapist and the patient on the number of hand movements (wrist and finger extensions) performed during therapy. The design implements resistive flex sensors for detecting the extensions and processes the information using the Lightblue bean microcontroller mounted on the wrist. Communication between the processing unit and display module is wireless and executes Bluetooth 4.0 communication protocol. The capacity for the glove to measure and record hand movements was tested on three stroke and one traumatic brain injured patient while performing a box and blocks test. During testing many design flaws were noted and several were adapted during testing to improve the function of the glove. Results of the testing showed that the glove could detect wrist and finger extensions but that the sensitivity had to be calibrated for each patient. It also allowed both the therapist and patient to know whether the patient was actually performing the task in the manner requested by the therapist. Further work will reveal whether this feedback can enhance recovery of hand function in neurologically impaired patients.