Benchmarking Wearable Bioelectronics: A Multimodal Evaluation Framework Integrating Parametric Regression, Machine Learning, and Fuzzy Logic Across Domain Applications

Abstract

This paper examines how a range of factors, including hardware architecture, sensor accuracy, battery lifetime, and user satisfaction, influence the performance and applicability of wearable technology. Through a study of 29 hardware and more than 2,300 hardware-enabled outcomes, we present a comprehensive analytical model, which includes ordinary least squares regression, nonlinear Random Forest feature extraction, and Takagi–Sugeno fuzzy logic inference. The models indicate that the four main performance indicators are heart rate accuracy, sleep tracking accuracy, battery life, and consumer satisfaction. Sensitivity analysis reveals that the hardware form factor is a unique technological architecture that no other hardware form factor can substitute for. People believe that denser hardware improves electronic quality; however, our analysis revealed that the more sensors a piece of hardware has, the lower its performance is (𝛽 = −0.542, p < .001). To facilitate the practical application of the study, we have created an application suitability matrix and rules for the decision-making process across four realms of study: clinical remote patient monitoring, longitudinal sleep research, military activities, and consumer wellness. While smart rings maximize sleep compliance through lower sensor density and longer battery life, smartwatches aim to deliver higher diagnostic output with more sophisticated systems. These results show that the effectiveness of wearables depends more on application-specific sensors than on high feature versatility, which guides the design of wearable bioelectronics.



Received: 14 August 2026 | Revised: 27 October 2026 | Accepted: 28 October 2026



Conflicts of Interest

The author declares that he has no conflicts of interest to this work.



Data Availability Statement

Data are available from the corresponding author upon reasonable request.



Author Contribution Statement

Hamza Saad: Conceptualization, Methodology, Software, Validation, Formal analysis, Investigation, Resources, Data curation, Writing – original draft, Writing – review & editing, Visualization, Supervision, Project administration.