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首页> 外文期刊>IEEE sensors journal >Multisensing Architecture for the Balance Losses During Gait via Physiologic Signals Recognition
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Multisensing Architecture for the Balance Losses During Gait via Physiologic Signals Recognition

机译：通过生理信号识别步态期间平衡损失的多抗体架构

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摘要

In this paper, we propose an innovative multi-sensor architecture operating in the field of pre-impact fall detection (PIFD). The proposed architecture jointly analyzes cortical and muscular involvement when unexpected slippages occur during steady walking. The electrophysiological signals (EEG and EMG) are acquired through wearable and wireless acquisition devices interfaced with a central control unit. The control unit consists of a hybrid architecture that exploits both an STM32L4 microcontroller and a DSP-oriented Simulink model. The EMG computation block translates EMGs into binary signals, which are used to trigger the cortical analyses, to extract a score on the revealed muscular pattern and to distinguish "standard" muscular behaviors from anomalous ones (perturbation). A Simulink model evaluates the cortical responsiveness in five bands of interest and implements a logical-based network to detect near-falls or potential ones. The proposed architecture goal is to obtain a detection time conservatively below 550 ms, which represents a strict limit for the successful application of postural recovery strategies. The system has been tested on 6 healthy subjects and demonstrated to react in 370.62 +/- 60.85 ms, while keeping competitive accuracy (96.21%).
机译：在本文中，我们提出了一种在预冲击落后检测（PIFD）领域的创新的多传感器架构。当在稳定行走期间发生意外滑动时，拟议的架构共同分析了皮质和肌肉作用。通过与中央控制单元接口的可穿戴和无线采集装置获取电生理信号（EEG和EMG）。控制单元包括混合架构，其利用STM32L4微控制器和DSP导向的Simulink模型。 EMG计算块将EMGS转换为二进制信号，该信号用于触发皮质分析，以提取显示的肌肉图案上的分数并区分“标准”从异常（扰动）上的肌肉行为。 Simulink模型在五个兴趣频段中评估皮质响应能力，并实现基于逻辑的网络以检测近乎跌倒或潜在的网络。所提出的架构目标是保守检测时间，保守低于550毫秒，这代表了成功应用姿势恢复策略的严格限制。该系统已在6个健康的科目上进行测试，并在370.62 +/- 60.85毫秒内进行了表决，同时保持竞争准确性（96.21％）。

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