The spectral analysis of signals is currently either dominated by the speed–accuracy trade-off or ignores a signal’s often non-stationary character. Here we introduce an open-source algorithm to calculate the fast continuous wavelet transform (fCWT). The parallel environment of fCWT separates scale-independent and scale-dependent operations, while utilizing optimized fast Fourier transforms that exploit downsampled wavelets. fCWT is benchmarked for speed against eight competitive algorithms, tested on noise resistance and validated on synthetic electroencephalography and in vivo extracellular local field potential data. fCWT is shown to have the accuracy of CWT, to have 100 times higher spectral resolution than algorithms equal in speed, to be 122 times and 34 times faster than the reference and fastest state-of-the-art implementations and we demonstrate its real-time performance, as confirmed by the real-time analysis ratio. fCWT provides an improved balance between speed and accuracy, which enables real-time, wide-band, high-quality, time–frequency analysis of non-stationary noisy signals.

When linked to wearable biosensors, Intelligent Environments could play a pivotal role in continuously monitoring and securing people’s well-being. We explored the value of one such biosensor that records Electrodermal Activity (EDA) by assessing its correlation with participants’ simultaneously, continuously, self-reported arousal. EDA’s frequency and amplitude of ‘non-specific’ Skin Conductance Responses in low, mid to high, or high levels of arousal were determined. When participants were in mid/high and high arousal situations, self-reports showed significant correlations (p < .001) with both EDA characteristics. With low arousal, no significant correlations were found. So, in cases of elevated stress, EDA shows the potential of being a reliable signal stress and, hence, also monitor of people’s well-being over time. Follow-up studies should further investigate and validate the utility of EDA monitoring as part of a comprehensive health monitoring strategy and its effectiveness in enhancing well-being.

The recent explosion of sensors enable our environment to act in an intelligent way. These Intelligent Environments rely on sense making of the sensors’ data streams. This process starts with reliable signal processing in real-time. This is challenging due to i) the low energy and computing resources of edge devices, ii) the signals’ non-stationary nature, and iii) the variety in software and hardware. To tackle this triplet of challenges, we present a WebAssembly-based hardware and software independent fast Continuous Wavelet Transform (fCWT), which excels in processing non-stationary signals at low costs. The application shows to be 2x-5.5x faster than competitors on speech, electrocardiogram (ECG), and vibration signals, enabling reliable real-time processing on edge devices. This yields new opportunities for the creation of safe and reliable Intelligent Environments.

Despite the wealthy of information biosignals cary, with Intelligent Environments (IE) they are often disregarded. We discuss issues we faced with integrating reliable biosignals into a real-world IE. These include the limited conductivity of dry sensors, movement artifacts, and placement issues. Subsequently, we introduce a real-time Signal Quality Indicator (SQI) for ElectroCardioGram (ECG), which consists of a Signal Loss Indicator (SLI) that detects signal capping, flatlining, high-frequency noise, and low-frequency noise. If the SLI detects a signal, the Signal Usability Indicator (SUI) subsequently processes the signal using the reference Pan-Tompkins algorithm and a dedicated filter to extract heart rate. The SQI marks what parts of the signal can and cannot be used for analysis. As such, it allows empirical calibration and, hence, the use of biosensors in real-world IE.