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Spiking neural networks (SNNs), a brain-inspired computing paradigm, are emerging for their inference performance, particularly in terms of energy efficiency and latency attributed to the plasticity in signal processing. To deploy SNNs in ubiquitous computing systems, signal encoding of sensors is crucial for achieving high accuracy and robustness. Using inertial sensor readings for gym activity recognition as a case study, this work comprehensively evaluates four main encoding schemes and deploys the corresponding SNN on the neuromorphic processor Loihi2 for postdeployment encoding assessment. Rate encoding, time-to-first-spike (TTFS) encoding, binary encoding, and delta modulation are evaluated using metrics such as average fire rate, signal-to-noise ratio (SNR), classification accuracy, robustness, and inference latency and energy. In this case study, the TTFS encoding required the lowest firing rate (2%) and achieved a comparative accuracy (89%) although it was the least robust scheme against error spikes (over 20% accuracy drop with 0.1 noisy spike rate). Rate encoding with optimal value-to-probability mapping achieved the highest accuracy (91.7%). Binary encoding provided a balance between information reconstruction and noise resistance. Multithreshold delta modulation showed the best robustness, with only a 0.7% accuracy drop at a 0.1 noisy spike rate. This work serves researchers in selecting the best encoding scheme for SNN-based ubiquitous sensor signal processing, tailored to specific performance requirements.

Urine output is a vital parameter to gauge kidney health. Current monitoring methods include manually written records, invasive urinary catheterization, or ultrasound measurements performed by highly skilled personnel. Catheterization bears high risks of infection while intermittent ultrasound measures and manual recording are time-consuming and might miss early signs of kidney malfunction. Bioimpedance (BI) measurements may serve as a noninvasive alternative for measuring urine volume in vivo. However, limited robustness has prevented its clinical translation. Here, a deep learning-based algorithm is presented that processes the local BI of the lower abdomen and suppresses artifacts to measure the bladder volume quantitatively, noninvasively, and without the continuous need for additional personnel. A tetrapolar BI wearable system was used to collect continuous bladder volume data from three healthy subjects to demonstrate the feasibility of operation, while clinical gold standards of urodynamic ( ${n}$ – 6) and uroflowmetry tests ( ${n}$ – 8) provided the ground truth. Optimized location for electrode placement and a model for the change in BI with changing bladder volume are deduced. The average error for full bladder volume estimation and for residual volume estimation was $-29\,\,\pm $ 87.6 mL, thus, comparable to commercial portable ultrasound devices (Bland Altman analysis showed a bias of −5.2 mL with LoA between 119.7 and −130.1 mL), while providing the additional benefit of hands-free, noninvasive, and continuous bladder volume estimation. The combination of the wearable BI sensor node and the presented algorithm provides an attractive alternative to current standard of care with potential benefits in providing insights into kidney function.

This article presents a low-power, self-sustainable, and modular wireless sensor node for aerod- ynamic and acoustic measurements on wind turbines and other industrial structures. It includes 40 high-accuracy barometers, ten microphones, and five differential pressure sensors and implements a lossy and a lossless on-board data compression algorithm to decrease the transmission energy cost. The wireless transmitter is based on Bluetooth Low Energy 5.1 (BLE) tuned for long range and high throu- ghput while maintaining adequate per-bit energy efficiency (80 nJ). Moreover, we field-assessed the node capability to collect precise and accurate aerodynamic data. Outdoor experimental tests revealed that the system can acquire and sustain a data rate of 850 kb\ $\ \text{s}$ over 438 m. The power consumption while collecting and streaming all the measured data is 120 mW, enabling self-sustainability and long-term in situ monitoring with a 111 cm 2 photovoltaic panel.