Accurate assessment of spasticity is crucial for physicians to select the most suitable treatment for patients. However, the current clinical practice standard is limited by imprecise assessment scales relying on perception. Here, we equipped the clinician with a portable, multimodal sensor glove to shift bedside evaluations from subjective perception to objective measurements. The measurements were correlated with biomechanical properties of muscles and revealed dynamic characteristics of spasticity, including catch symptoms and velocity-dependent resistance. Using the biomechanical data, a radar metric was developed for ranking severity in spastic knees and elbows. The continuous monitoring results during anesthesia induction enable the separation of neural and structural contributions to spasticity in 21 patients. This work delineated effects of reflex excitations from structural abnormalities, to classify underlying causes of spasticity that will inform treatment decisions for evidence-based patient care.

Hearing aids help overcome the challenges associated with hearing loss, and thus greatly benefit and improve the lives of those living with hearing-impairment. Unfortunately, there is a lack of adoption of hearing aids among those that can benefit from hearing aids. Hearing researchers and audiologists are trying to address this problem through their research. However, the current proprietary hearing aid market makes it difficult for academic researchers to translate their findings into commercial use. In order to abridge this gap and accelerate research in hearing health care, we present the design and implementation of the Open Speech Platform (OSP), which consists of a co-design of open-source hardware and software. The hardware meets the industry standards and enables researchers to conduct experiments in the field. The software is designed with a systematic and modular approach to standardize algorithm implementation and simplify user interface development. We evaluate the performance of OSP regarding both its hardware and software, as well as demonstrate its usefulness via a self-fitting study involving human participants.

We have previously reported a realtime, open-source speech-processing platform (OSP) for hearing aids (HAs) research. In this contribution, we describe a wearable version of this platform to facilitate audiological studies in the lab and in the field. The system is based on smartphone chipsets to leverage power efficiency in terms of FLOPS/watt and economies of scale. We present the system architecture and discuss salient design elements in support of HA research. The ear-level assemblies support up to 4 microphones on each ear, with 96 kHz, 24 bit codecs. The wearable unit runs OSP Release 2018c on top of 64-bit Debian Linux for binaural HA with an overall latency of 5.6 ms. The wearable unit also hosts an embedded web server (EWS) to monitor and control the HA state in realtime. We describe three example web apps in support of typical audiological studies they enable. Finally, we describe a baseline speech enhancement module included with Release 2018c, and describe extensions to the algorithms as future work.