Optimized Sampling Rate for Voltammetry-Based Electrochemical Sensing in Wearable and IoT Applications

The recent advancements in electrochemical measurements are guiding the development of new platforms for in-situ point-of-care monitoring of human-metabolite, markers and drugs. Despite this, the application of Voltammetry-Based Sensing (VBS) techniques is still limited in wearable, portable, or IoT systems. In order to use VBS approaches to measure analytes in small and low-power electronic platforms for diagnostics, several improvements are required. For example, the definition of a method to achieve the right trade-off between sample rate and sensing performance is still missing. To develop a method to define the best sampling rate, we present here an extensive analysis of experimental data to prove that is feasible to detect drugs such as paracetamol by Staircase Cyclic Voltammetry (SCV) or Differential Pulse Voltammetry (DVP) direct detection methods, with low sampling frequency. Our results prove that the proposed method helps the development of systems capable of discriminating the minimum pharmacology concentration of the metabolite under analysis with a massive reduction of the sampling frequency

An Intelligent In-Shoe System for Gait Monitoring and Analysis with Optimized Sampling and Real-Time Visualization Capabilities

The deterioration of gait can be used as a biomarker for ageing and neurological diseases. Continuous gait monitoring and analysis are essential for early deficit detection and personalized rehabilitation. The use of mobile and wearable inertial sensor systems for gait monitoring and analysis have been well explored with promising results in the literature. However, most of these studies focus on technologies for the assessment of gait characteristics, few of them have considered the data acquisition bandwidth of the sensing system. Inadequate sampling frequency will sacrifice signal fidelity, thus leading to an inaccurate estimation especially for spatial gait parameters. In this work, we developed an inertial sensor based in-shoe gait analysis system for real-time gait monitoring and investigated the optimal sampling frequency to capture all the information on walking patterns. An exploratory validation study was performed using an optical motion capture system on four healthy adult subjects, where each person underwent five walking sessions, giving a total of 20 sessions. Percentage mean absolute errors (MAE) obtained in stride time, stride length, stride velocity, and cadence while walking were 1.19, 1.68, 2.08, and 1.23, respectively. In addition, an eigenanalysis based graphical descriptor from raw gait cycle signals was proposed as a new gait metric that can be quantified by principal component analysis to differentiate gait patterns, which has great potential to be used as a powerful analytical tool for gait disorder diagnostics