Myoelectric-Based Estimation of Vertical Ground Reaction Force During Unconstrained Walking by a Stacked One-Dimensional Convolutional Long Short-Term Memory Model.

The volitional control of powered assistive devices is commonly performed by mapping the electromyographic (EMG) activity of the lower limb to joints' angular kinematics, which are then used as the input for regulation. However, during walking, the ground reaction force (GRF) plays a central role in the modulation of the gait, providing dynamic stability and propulsion during the stance phase. Including this information within the control loop of prosthetic devices can improve the quality of the final output, providing more physiological walking dynamics that enhances the usability and patient comfort.

A Review on Assisted Living Using Wearable Devices.

Forecasts about the aging trend of the world population agree on identifying increased life expectancy as a serious risk factor for the financial sustainability of social healthcare systems if not properly supported by innovative care management policies. Such policies should include the integration within traditional healthcare services of assistive technologies as tools for prolonging healthy and independent living at home, but also for introducing innovations in clinical practice such as long-term and remote health monitoring. For their part, solutions for active and assisted living have now reached a high degree of technological maturity, thanks to the considerable amount of research work carried out in recent years to develop highly reliable and energy-efficient wearable sensors capable of enabling the development of systems to monitor activity and physiological parameters over time, and in a minimally invasive manner.

Phasor-Based Myoelectric Synergy Features: A Fast Hand-Crafted Feature Extraction Scheme for Boosting Performance in Gait Phase Recognition.

Gait phase recognition systems based on surface electromyographic signals (EMGs) are crucial for developing advanced myoelectric control schemes that enhance the interaction between humans and lower limb assistive devices. However, machine learning models used in this context, such as Linear Discriminant Analysis (LDA) and Support Vector Machine (SVM), typically experience performance degradation when modeling the gait cycle with more than just stance and swing phases. This study introduces a generalized phasor-based feature extraction approach (PHASOR) that captures spatial myoelectric features to improve the performance of LDA and SVM in gait phase recognition.

The PI3Kδ inhibitor zandelisib on intermittent dosing in relapsed/refractory follicular lymphoma: Results from a global phase 2 study.

In this global phase 2 study in patients with relapsed/refractory follicular lymphoma (FL), zandelisib was administered on intermittent dosing to mitigate immune-related adverse events and infections that have been reported with oral PI3Kδ inhibitors administered daily continuously. Eligible patients with measurable disease and progression after at least two prior therapies were administered zandelisib until disease progression or intolerability. The primary efficacy endpoint was objective response rate (ORR) and the key secondary efficacy endpoint was duration of response (DOR).

A Computer-Aided Screening Solution for the Identification of Diabetic Neuropathy From Standing Balance by Leveraging Multi-Domain Features.

The early diagnosis of diabetic neuropathy (DN) is fundamental in order to enact timely therapeutic strategies for limiting disease progression. In this work, we explored the suitability of standing balance task for identifying the presence of DN. Further, we proposed two diagnosis pathways in order to succeed in distinguishing between different stages of the disease.