A highly sensitive wearable pressure sensor capsule based on PVA/Mxene composite gel.

Wearable sensors have drawn considerable interest in the recent research world. However, simultaneously realizing high sensitivity and wide detection limits under changing surrounding environment conditions remains challenging. In the present study, we report a wearable piezoresistive pressure sensor capsule that can detect pulse rate and human motion. The capsule includes a flexible silicon cover and is filled with different PVA/MXene (PVA-Mx) composites by varying the weight percentage of MXene in the polymer matrix. Different characterizations such as XRD, FTIR and TEM results confirm that the PVA-Mx silicon capsule was successfully fabricated. The PVA-Mx gel-based sensor capsule remarkably endows a low detection limit of 2 kPa, exhibited high sensitivity of 0.45 kPa-1 in the ranges of 2-10 kPa, and displayed a response time of ~ 500 ms, as well as good mechanical stability and non-attenuating durability over 500 cycles. The piezoresistive sensor capsule sensor apprehended great stability towards changes in humidity and temperature. These findings substantiate that the PVA/MXene sensor capsule is potentially suitable for wearable electronics and smart clothing.

A review of smart sensors coupled with Internet of Things and Artificial Intelligence approach for heart failure monitoring.

Over the last decade, there has been a huge demand for health care technologies such as sensors-based prediction using digital health. With the continuous rise in the human population, these technologies showed to be potentially effective solutions to life-threatening diseases such as heart failure (HF). Besides being a potential for early death, HF has a significantly reduced quality of life (QoL). Heart failure has no cure. However, treatment can help you live a longer and more active life with fewer symptoms. Thus, it is essential to develop technological aid solutions allowing early diagnosis and consequently, effective treatment with possibly delayed mortality. Commonly, forecasts of HF are based on the generation of vast volumes of data usually collected from an individual patient by different components of the family history, physical examination, basic laboratory results, and other medical records. Though, these data are not effectively useful for predicting this failure, nevertheless, with the aid of advanced medical technology such as interconnected multi-sensory-based devices, and based on several medical history characteristics, the broad data provided machine learning algorithms to predict risk factors for heart disease of an individual is beneficial. There will be many challenges for the next decade of advancements in HF care: exploiting an increasingly growing repertoire of interconnected internal and external sensors for the benefit of patients and processing large, multimodal datasets with new Artificial Intelligence (AI) software. Various methods for predicting heart failure and, primarily the significance of invasive and non-invasive sensors along with different strategies for machine learning to predict heart failure are presented and summarized in the present study.

Cortical bone drilling: An experimental and numerical study.

BACKGROUND: Bone drilling is a common surgical procedure in orthopedics, dental and neurosurgeries. In conventional bone drilling process, the surgeon exerts a considerable amount of pressure to penetrate the drill into the bone tissue. Controlled penetration of drill in the bone is necessary for safe and efficient drilling. OBJECTIVE: Development of a validated Finite Element (FE) model of cortical bone drilling. METHODS: Drilling experiments were conducted on bovine cortical bone. The FE model of the bone drilling was based on mechanical properties obtained from literature data and additionally conducted microindentation tests on the cortical bone. RESULTS: The magnitude of stress in bone was found to decrease exponentially away from the lips of the drill in simulations. Feed rate was found to be the main influential factor affecting the force and torque in the numerical simulations and experiments. The drilling thrust force and torque were found to be unaffected by the drilling speed in numerical simulations. Simulated forces and torques were compared with experimental results for similar drilling conditions and were found in good agreement.CONCLUSIONS: FE schemes may be successfully applied to model complex kinematics of bone drilling process.