BioSensics (United States)

To demonstrate the efficacy of digital speech analysis as a tool for identifying and gauging cognitive impairment in older adults by focusing on speech biomarkers.

<h3>Objective:</h3> To develop a standardized, clinically-validated digital solution for rigorous, longitudinal assessment of ocular weakness and dysarthria in myasthenia gravis (MG), which can be applied remotely. <h3>Background:</h3> MG is a chronic autoimmune neuromuscular disease characterized by fluctuating weakness of skeletal muscle, which frequent affects ocular muscles and speech. Current assessments of myasthenic weakness require neurologic expertise, are time-consuming and can still be subjective. Additionally, measured at one point in time, assessments provide a snapshot of a patient’s MG, which may not adequately reflect the spectrum of fluctuating weakness. Ratings of ocular weakness and dysarthria are particularly subjective. These shortcomings of current outcome measures limit performance of clinical trials and provide a limited view of the patient’s clinical status. <h3>Design/Methods:</h3> We developed a tablet-based digital health solution that captures video data collected by patients and MG-specific patient-reported outcome measures. Ptosis and dysarthria are rated asynchronously. The tablet provides text instruction and audio prompts to the patient and data are uploaded to a cloud. Predetermined measures of success have been defined. <h3>Results:</h3> A pilot study with a target sample size of 20 individuals with MG is ongoing to determine the feasibility of using the proposed solution as a health monitoring platform for MG. Two MG experts will rate speech and video data for dysarthria and ptosis based on the Quantitative Myasthenia Gravis (QMG) scale. Association between the extracted speech and facial characteristics to clinical scores of dysarthria and ptosis will be investigated. <h3>Conclusions:</h3> Despite considerable recent advances in treatments for MG, rigorous, longitudinal tools for monitoring of its characteristic fluctuating weakness remain limited and represent an unmet need. The feasibility, validity and acceptability of the proposed digital solution will be determined for 20 patients with MG. If successful, this technology will be further developed and studied in a larger observational study. <b>Disclosure:</b> An immediate family member of Dr. Guidon has received personal compensation for serving as an employee of GE Healthcare. Dr. Guidon has received personal compensation in the range of $500-$4,999 for serving on a Scientific Advisory or Data Safety Monitoring board for Alexion. Dr. Guidon has received personal compensation in the range of $500-$4,999 for serving on a Scientific Advisory or Data Safety Monitoring board for Momenta. Dr. Guidon has received personal compensation in the range of $500-$4,999 for serving on a Scientific Advisory or Data Safety Monitoring board for Argenx. The institution of Dr. Guidon has received research support from MGFA. The institution of Dr. Guidon has received research support from Project Data Sphere. The institution of Dr. Guidon has received research support from MGNet. The institution of Dr. Guidon has received research support from MGNet. Dr. Guidon has received publishing royalties from a publication relating to health care. Dr. Mishra has received personal compensation for serving as an employee of Biosensics. Miss Lindsay has nothing to disclose. Mr. Mulukutla has received personal compensation for serving as an employee of BioSensics LLC. Dr. Vaziri has received personal compensation for serving as an employee of Biosensics.

The study presents a novel approach to objectively assessing the upper-extremity motor symptoms in spinocerebellar ataxia (SCA) using data collected via a wearable sensor worn on the patient’s wrist during upper-extremity tasks associated with the Assessment and Rating of Ataxia (SARA). First, we developed an algorithm for detecting/extracting the cycles of the finger-to-nose test (FNT). We extracted multiple features from the detected cycles and identified features and parameters correlated with the SARA scores. Additionally, we developed models to predict the severity of symptoms based on the FNT. The proposed technique was validated on a dataset comprising the seventeen (n = 17) participants’ assessments. The cycle detection technique showed an accuracy of 97.6% in a Bland–Altman analysis and a 94% accuracy (F1-score of 0.93) in predicting the severity of the FNT. Furthermore, the dependency of the upper-extremity tests was investigated through statistical analysis, and the results confirm dependency and potential redundancies in the upper-extremity SARA assessments. Our findings pave the way to enhance the utility of objective measures of SCA assessments. The proposed wearable-based platform has the potential to eliminate subjectivity and inter-rater variabilities in assessing ataxia.

Assessment of instrumental activities of daily living (IADL) is essential for the diagnosis and staging of dementia. However, current IADL assessments are subjective and cannot be administered remotely. We proposed a smart-home design, called IADLSys, for remote monitoring of IADL. IADLSys consists of three major components: (1) wireless physical tags (pTAG) attached to objects of interest, (2) a pendant–sensor to monitor physical activities and detect interaction with pTAGs, and (3) an interactive tablet as a gateway to transfer data to a secured cloud. Four studies, including an exploratory clinical study with five older adults with clinically confirmed cognitive impairment, who used IADLSys for 24 h/7 days, were performed to confirm IADLSys feasibility, acceptability, adherence, and validity of detecting IADLs of interest and physical activity. Exploratory tests in two cases with severe and mild cognitive impairment, respectively, revealed that a case with severe cognitive impairment either overestimated or underestimated the frequency of performed IADLs, whereas self-reporting and objective IADL were comparable for the case with mild cognitive impairment. This feasibility and acceptability study may pave the way to implement the smart-home concept to remotely monitor IADL, which in turn may assist in providing personalized support to people with cognitive impairment, while tracking the decline in both physical and cognitive function.

We propose a novel multivariate time series autoencoder, which produces interpretable linear-dynamical latent features that govern the predictions for several downstream tasks. To this end, we combine a transformer autoencoder with a dynamical atoms-based autoencoder to mimic Koopman operators in the latent space. We demonstrate that our approach significantly outperforms deep Koopman operator learning baselines for time series forecasting on chaotic systems such as the lorenz Attractor. Furthermore, the dynamics-aware representations, combined with a transformer classifier, lead to state-of-the-art classification accuracy on benchmark multivariate time series datasets. Our code is publicly available at https://github.com/mlpotter/T-DYAN-T.

To explore the use of wearable sensors for objective measurement of motor impairment in spinocerebellar ataxia (SCA) patients during clinical assessments of gait and balance. In total, 14 patients with genetically confirmed SCA (mean age 61.6 ± 8.6years) and 4 healthy controls (mean age 49.0 ± 16.4years) were recruited through the Massachusetts General Hospital (MGH) Ataxia Center. Participants donned seven inertial sensors while performing two independent trials of gait and balance assessments from the Scale for the Assessment and Rating of Ataxia (SARA) and Brief Ataxia Rating Scale (BARS2). Univariate analysis was used to identify sensor-derived metrics from wearable sensors that discriminate motor function between the SCA and control groups. Multivariate linear regression models were used to estimate the subjective in-person SARA/BARS2 ratings. Spearman correlation coefficients were used to evaluate the performance of the model. Stride length variability, stride duration, cadence, stance phase, pelvis sway, and turn duration were different between SCA and controls (p < 0.05). Similarly, sway and sway velocity of the ankle, hip, and center of mass differentiated SCA and controls (p < 0.05). Using these features, linear regression models showed moderate-to-strong correlation with clinical scores from the in-person rater during SARA assessments of gait (r = 0.73, p = 0.003) and stance (r = 0.90, p < 0.001) and the BARS2 gait assessment (r = 0.74, p = 0.003). This study demonstrates that sensor-derived metrics can potentially be used to estimate the level of motor impairment in patient with SCA quickly and objectively. Thus, digital biomarkers from wearable sensors have the potential to be an integral tool for SCA clinical trials and care.

We describe the design and control of a wearable robotic device powered by pneumatic artificial muscle actuators for use in ankle–foot rehabilitation. The design is inspired by the biological musculoskeletal system of the human foot and lower leg, mimicking the morphology and the functionality of the biological muscle–tendon–ligament structure. A key feature of the device is its soft structure that provides active assistance without restricting natural degrees of freedom at the ankle joint. Four pneumatic artificial muscles assist dorsiflexion and plantarflexion as well as inversion and eversion. The prototype is also equipped with various embedded sensors for gait pattern analysis. For the subject tested, the prototype is capable of generating an ankle range of motion of 27° (14° dorsiflexion and 13° plantarflexion). The controllability of the system is experimentally demonstrated using a linear time-invariant (LTI) controller. The controller is found using an identified LTI model of the system, resulting from the interaction of the soft orthotic device with a human leg, and model-based classical control design techniques. The suitability of the proposed control strategy is demonstrated with several angle-reference following experiments.