Effect of sensor number and location on accelerometry-based vertical ground reaction force estimation during walking.

Knee osteoarthritis is a major cause of global disability and is a major cost for the healthcare system. Lower extremity loading is a determinant of knee osteoarthritis onset and progression; however, technology that assists rehabilitative clinicians in optimizing key metrics of lower extremity loading is significantly limited. The peak vertical component of the ground reaction force (vGRF) in the first 50% of stance is highly associated with biological and patient-reported outcomes linked to knee osteoarthritis symptoms. Monitoring and maintaining typical vGRF profiles may support healthy gait biomechanics and joint tissue loading to prevent the onset and progression of knee osteoarthritis. Yet, the optimal number of sensors and sensor placements for predicting accurate vGRF from accelerometry remains unknown. Our goals were to: 1) determine how many sensors and what sensor locations yielded the most accurate vGRF loading peak estimates during walking; and 2) characterize how prescribing different loading conditions affected vGRF loading peak estimates. We asked 20 young adult participants to wear 5 accelerometers on their waist, shanks, and feet and walk on a force-instrumented treadmill during control and targeted biofeedback conditions prompting 5% underloading and overloading vGRFs. We trained and tested machine learning models to estimate vGRF from the various sensor accelerometer inputs and identified which combinations were most accurate. We found that a neural network using one accelerometer at the waist yielded the most accurate loading peak vGRF estimates during walking, with average errors of 4.4% body weight. The waist-only configuration was able to distinguish between control and overloading conditions prescribed using biofeedback, matching measured vGRF outcomes. Including foot or shank acceleration signals in the model reduced accuracy, particularly for the overloading condition. Our results suggest that a system designed to monitor changes in walking vGRF or to deploy targeted biofeedback may only need a single accelerometer located at the waist for healthy participants.

Aptasensor for Detection of Influenza-A in Human Saliva.

Access to low-cost, rapid, individualized diagnostics at point-of-care and point-of-need is vital to minimize the impact of highly infectious viruses, such as influenza. Herein, a biosensor for detecting hemagglutinin (HA), an abundant capsid protein in H1N1 viruses, is demonstrated. A gold working electrode was functionalized with a thiol-modified, HA-binding aptamer derivatized with a methylene blue modification for redox reporting. The aptamer was characterized by surface plasmon resonance to confirm its biorecognition activity for HA. The aptasensor was characterized by square wave voltammetry to quantify the sensor's response to varying concentrations of HA. The sensor exhibited a lower limit of detection of 1.5 pM with linear detection of up to 1.2 nM in both Tris buffer and simulated human saliva, thus encompassing the clinically relevant HA range in saliva. Average sensitivity was measured at 21.083 nA·nM-1in Tris and 14.5 nA·nM-1in artificial saliva across clinically relevant HA titers. Sensor stability across time was also investigated, providing a preliminary understanding of the translational viability of the aptasensors for mobile and remote diagnostic applications.

Preliminary Evaluation of a Solar-Powered Wristband for Continuous Multi-Modal Electrochemical Monitoring.

Continuous, non-invasive wearable measurement of metabolic biomarkers could provide vital insight into patient condition for personalized health and wellness monitoring. We present our efforts towards developing a wearable solar-powered electrochemical platform for multimodal sweat based metabolic monitoring. This wrist-worn wearable system consists of a flexible photovoltaic cell connected to a circuit board containing ultra low power circuitry for sensor data collection, energy harvesting, and wireless data transmission, all integrated into an elastic fabric wristband. The system continuously samples amperometric, potentiometric, temperature, and motion data and wirelessly transmits these to a data aggregator. The full wearable system is 7.5 cm long and 5 cm in diameter, weighs 22 grams, and can run directly from harvested light energy. Relatively low levels of light such as residential lighting (∼200 lux) are sufficient for continuous operation of the system. Excess harvested energy is stored in a small 37 mWh lithium polymer battery. The battery can be charged in ∼14 minutes under full sunlight and can power the system for ∼8 days when fully charged. The system has an average power consumption of 176 µW. The solar-harvesting performance of the system was characterized in a variety of lighting conditions, and the amperometric and potentiometric electrochemical capabilities of the system were validated in vitro.Clinical relevance-The presented solar-powered wearable system enables continuous wireless multi-modal electrochemical monitoring for uninterrupted sensing of metabolic biomarkers in sweat while harvesting energy from indoor lighting or sunlight.