Gait speed is an important parameter to characterize people's daily mobility. For real-world speed measurement, inertial sensors or global navigation satellite system (GNSS) can be used on wrist, possibly integrated in a wristwatch. However, power consumption of GNSS is high and data are only available outdoor. Gait speed estimation using wrist-mounted inertial sensors is generally based on machine learning and suffers from low accuracy because of the inadequacy of using limited training data to build a general speed model that would be accurate for the whole population. To overcome this issue, a personalized model was proposed, which took unique gait style of each subject into account. Cadence and other biomechanically derived gait features were extracted from a wrist-mounted accelerometer and barometer. Gait features were fused with few GNSS data (sporadically sampled during gait) to calibrate the step length model of each subject through online learning. The proposed method was validated on 30 healthy subjects where it has achieved a median [Interquartile Range] of root mean square error of 0.05 [0.04–0.06] (m/s) and 0.14 [0.11–0.17] (m/s) for walking and running, respectively. Results demonstrated that the personalized model provided similar performance as GNSS. It used 50 times less training GNSS data than nonpersonalized method and achieved even better results. This parsimonious GNSS usage allowed extending battery life. The proposed algorithm met requirements for applications which need accurate, long, real-time, low-power, and indoor/outdoor speed estimation in daily life.

中文翻译：

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使用腕部传感器进行真实步态速度估计：一种个性化方法

步态速度是表征人们日常活动能力的重要参数。对于现实世界中的速度测量，惯性传感器或全球导航卫星系统（GNSS）可以在手腕上使用，可能集成在手表中。但是，GNSS的功耗很高，并且数据仅在室外可用。使用腕上安装的惯性传感器的步态速度估计通常基于机器学习，并且由于使用有限的训练数据来建立对整个人群都准确的通用速度模型的不足而导致准确性低下。为了克服这个问题，提出了一种个性化的模型，该模型考虑了每个主体的独特步态风格。从腕上安装的加速度计和气压计中提取踏频和其他生物力学得出的步态特征。步态特征与少量GNSS数据（在步态中偶尔采样）融合在一起，以通过在线学习校准每个受试者的步长模型。该方法在30位健康受试者中得到了验证，该受试者的行走均方根误差的中位数[四分位间距]为0.05 [0.04-0.06]（m / s）和0.14 [0.11-0.17]（m / s）和运行。结果表明，个性化模型提供了与GNSS相似的性能。与非个性化方法相比，它使用的训练GNSS数据少50倍，并且获得了甚至更好的结果。这种简化的GNSS使用方式可以延长电池寿命。所提出的算法满足了在日常生活中需要准确，长，实时，低功耗以及室内/室外速度估计的应用的要求。