De Luca Chiara, Tincani Mirco, Indiveri Giacomo, Donati Elisa
Institute of Neuroinformatics, University of Zurich and ETH Zurich, Zurich, Switzerland.
Digital Society Initiative, University of Zurich, Zurich, Switzerland.
Npj Unconv Comput. 2025;2(1):6. doi: 10.1038/s44335-025-00024-6. Epub 2025 Apr 2.
With the advent of novel sensor and machine learning technologies, it is becoming possible to develop wearable systems that perform continuous recording and processing of biosignals for health or body state assessment. For example, modern smartwatches can already track physiological functions, including heart rate and its anomalies, with high precision. However, stringent constraints on size and energy consumption pose significant challenges for always-on operation to detect trends across multiple time scales for extended periods of time. To address these challenges, we propose an alternative solution that exploits the ultra-low power consumption features of mixed-signal neuromorphic technologies. We present a biosignal processing architecture that integrates multimodal sensory inputs and processes them using the principles of neural computation to reliably detect trends in heart rate and physiological states. We validate this architecture on a mixed-signal neuromorphic processor and demonstrate its robust operation despite the inherent variability of the analog circuits present in the system. In addition, we demonstrate how the system can process multi scale signals, namely instantaneous heart rate and its long-term states discretized into distinct zones, effectively detecting monotonic changes over extended periods that indicate pathological conditions such as agitation. This approach paves the way for a new generation of energy-efficient stand-alone wearable devices that are particularly suited for scenarios that require continuous health monitoring with minimal device maintenance.
随着新型传感器和机器学习技术的出现，开发可穿戴系统以对生物信号进行连续记录和处理以用于健康或身体状态评估成为可能。例如，现代智能手表已经能够高精度地追踪生理功能，包括心率及其异常情况。然而，对尺寸和能耗的严格限制给长时间持续运行以检测多个时间尺度上的趋势带来了重大挑战。为应对这些挑战，我们提出了一种替代解决方案，该方案利用了混合信号神经形态技术的超低功耗特性。我们展示了一种生物信号处理架构，该架构集成了多模态感官输入，并使用神经计算原理对其进行处理，以可靠地检测心率和生理状态的趋势。我们在混合信号神经形态处理器上验证了该架构，并证明了尽管系统中存在模拟电路固有的可变性，它仍能稳健运行。此外，我们展示了该系统如何处理多尺度信号，即瞬时心率及其离散为不同区域的长期状态，有效检测长时间内指示诸如躁动等病理状况的单调变化。这种方法为新一代节能独立可穿戴设备铺平了道路，这些设备特别适用于需要以最少设备维护进行连续健康监测的场景。
