Design and Implementation of an Ultra-Low Resource Electrodermal Activity Sensor for Wearable Applications ‡

Abstract

While modern low-power microcontrollers are a cornerstone of wearable physiological sensors, their limited on-chip storage typically makes peripheral storage devices a requirement for long-term physiological sensing—significantly increasing both size and power consumption. Here, a wearable biosensor system capable of long-term recording of physiological signals using a single, 64 kB microcontroller to minimize sensor size and improve energy performance is described. Electrodermal (EDA) signals were sampled and compressed using a multiresolution wavelet transformation to achieve long-term storage within the limited memory of a 16-bit microcontroller. The distortion of the compressed signal and errors in extracting common EDA features is evaluated across 253 independent EDA signals acquired from human volunteers. At a compression ratio (CR) of 23.3×, the root mean square error (RMSErr) is below 0.016 μ S and the percent root-mean-square difference (PRD) is below 1%. Tonic EDA features are preserved at a CR = 23.3× while phasic EDA features are more prone to reconstruction errors at CRs > 8.8×. This compression method is shown to be competitive with other compressive sensing-based approaches for EDA measurement while enabling on-board access to raw EDA data and efficient signal reconstructions. The system and compression method provided improves the functionality of low-resource microcontrollers by limiting the need for external memory devices and wireless connectivity to advance the miniaturization of wearable biosensors for mobile applications.

Innovations in Electrodermal Activity Data Collection and Signal Processing: A Systematic Review

“…The prototype ring seemed to be a promising wearable tool for future studies. Recently, the design and implementation of an ultra-low resource EDA sensor for wearable applications incorporated a compression method [39]. Although theirs is a DC-source topology, the system and compression method could improve the functionality of low-resource microcontrollers.…”

A Systematic Review of Sensing and Differentiating Dichotomous Emotional States Using Audio-Visual Stimuli

“…The EDA measurements are simple and are easy to install [102] but are influenced by external factors such as temperature and humidity [102], [103]. ECG generates a higher magnitude output signal compared to other methods.…”

Biosignal Compression Toolbox for Digital Biomarker Discovery

“…There is a tradeoff between compression of a signal (compression ratio) and minimizing information loss [ 14 ]. Data compression has been explored for biomedical signals previously, but the evaluation of these methods has been limited to one type of sensor, including either electrocardiogram (ECG) [ 14 , 15 , 16 , 17 ], photoplethysmography (PPG) [ 18 , 19 , 20 ], accelerometry [ 21 , 22 ], and electrodermal activity (EDA) [ 23 ]. Data compression methods can be divided into two groups: Lossless compression and lossy compression.…”

“…SVD is used to factorize the signal into three smaller sets of values [ 27 ], which preserves features for digital biomarker development. BD-WT is widely used in ECG data compression [ 14 , 16 , 23 , 28 ]. DCT-DOST is a more recent method that has shown to be very robust for ECG signal compression [ 25 ].…”