内容来源：https://research.google/blog/unlocking-health-insights-estimating-advanced-walking-metrics-with-smartwatches/

内容总结：

谷歌研究团队近日在智能手表健康监测领域取得重要突破。通过一项大规模验证研究，科学家证实普通智能手表能够高精度、高可靠性地估算一系列关键步行参数，为无创健康监测提供了全新可能。

步行参数，如步速、步长和双脚支撑时间，是评估人体整体健康状况、跌倒风险以及神经或肌肉骨骼疾病进展的重要生物标志。传统测量依赖昂贵的实验室设备，难以实现持续追踪。虽然智能手机可通过内置传感器进行测量，但其精度受放置位置影响较大。相比之下，佩戴位置固定的智能手表为持续监测提供了更便捷、稳定的平台。

在题为《基于智能手表的步行参数估算》的研究中，谷歌科学家开发了一种基于时序卷积网络的多输出深度学习模型。该模型可直接处理用户身高和手表内置惯性传感器的原始数据，并同步估算出步速、步长、摆动时间、站立时间及双脚支撑时间等多项关键指标，无需进行容易产生误差的数据积分运算。

为验证模型效能，研究团队在美国和日本两地招募了246名参与者，收集了约7万段步行数据，并使用实验室级步道分析系统作为基准进行对比。测试涵盖了常规步行、六分钟步行测试及佩戴膝撑模拟步态不对称等多种场景。

研究结果显示，基于智能手表的估算方法在大多数指标上表现出高度的有效性和出色的可靠性，其精度与基于智能手机的估算方法相当。特别值得注意的是，提供用户身高数据显著提升了手表对步速和步长的估算准确性。

这项成果标志着可穿戴设备在健康监测应用方面迈出了关键一步。将专业的步态分析从实验室延伸至手腕，使得在日常生活场景中持续、便捷地监测健康状况成为可能，为疾病早期预警、跌倒风险预防及个性化康复管理开辟了新路径。

研究团队表示，未来将继续优化和扩展可监测的指标范围，以进一步提升智能手表在主动健康管理中的实用价值。

中文翻译：

解锁健康洞察：利用智能手表估算高级步行指标
2026年1月15日
谷歌研究科学家 Amir Farjadian 与高级研究科学家 Ming-Zher Poh

我们通过一项大规模验证研究证实，智能手表可作为估算时空步态指标的高度可靠平台。

快速了解
步态指标——如步行速度、步长和双支撑时间（即双脚同时接触地面在步态周期中的占比）——是评估个人整体健康状况、跌倒风险以及神经或肌肉骨骼疾病进展的关键生物标志物。对步行方式的分析（即步态分析）能够以无创方式为整体健康、损伤及健康问题提供宝贵洞察。

以往，测量步态需要昂贵、专业的实验室设备，难以实现持续追踪。虽然如今智能手机可通过内置惯性测量单元（IMU）提供便携替代方案，但其需精确放置（如大腿口袋或腰带）才能获得最准确结果。相比之下，智能手表固定佩戴于手腕，为持续追踪提供了更实用、更稳定的平台，甚至可将追踪场景扩展至无手机环境（如在屋内行走）。

尽管具备这一关键的实际优势，智能手表在综合步态指标评估方面长期落后于智能手机。在我们的研究《基于智能手表的步行指标估算》中，我们致力于弥补这一差距。我们证明，消费级智能手表是估算全套时空步态指标的高度可行、准确且可靠的平台，其性能与基于智能手机的方法相当。

面向腕部数据的深度学习方法
为实现这一目标，我们开发了一种基于时序卷积网络（TCN）架构的多输出（即多头）深度学习模型，该架构同时适用于智能手表和智能手机数据。这种多头模型与以往基于TCN的方法（通常仅提供时间事件如触地点，需通过易漂移的积分计算步长、步速等空间指标）形成关键区别。我们的模型则直接估算所有时空步态指标。

模型接收两项关键输入：用户身高（单一标量人口统计输入）和原始IMU信号（包括单只腕戴Pixel Watch以50Hz采样频率采集的三轴加速度计和三轴陀螺仪数据）。模型架构从IMU传感器输入中提取特征嵌入，再与人口统计数据拼接后输入最终预测层。多头模型直接输出全套指标估算值，包括：双侧指标（步速和双支撑时间各一个输出头），以及单侧指标（步长、摆动时间和站立时间各设两个输出头，分别对应左、右脚）。各项指标定义如下：

• 步速：行走距离除以所用时间（单位：厘米/秒）
• 双支撑时间：步态周期中双脚同时接触地面的占比（单位：百分比）
• 步长（单侧）：从一只脚首次触地到另一只脚首次触地的距离（单位：厘米）
• 摆动时间（单侧）：步态周期中脚部未接触地面的时长（单位：毫秒）
• 站立时间（单侧）：步态周期中脚部接触地面的时长（单位：毫秒）

数据分割采用5秒窗口及1秒重叠步长。损失函数选用平均绝对百分比误差（MAPE），该函数能针对多单位输出（如以厘米为单位的步长、以毫秒为单位的双支撑时间）优化相对准确性。

大规模研究的严格验证
为严格评估模型，我们开展了一项大规模验证研究，涵盖246名参与者及约7万段步行数据。参与者需年满18岁、未使用辅助设备且无影响平衡或步态的疾病。数据来自两个国际队列：美国加州山景城的谷歌团队和日本京都大学。

参考测量（真实值）采用实验室级Zeno步道系统。参与者双手腕各佩戴一只Pixel Watch 1，并在前袋、后袋、背包及斜挎包中放置四部Pixel 6手机。

研究方案包含多样化的步行模式以确保全面评估：

• 六分钟步行测试：以自选速度沿步道完成绕圈行走
• 快速步行：以舒适且稳定的较快速度行走
• 轻度及中度不对称步行：佩戴锁定特定屈曲/伸展角度的铰链式护膝进行自选速度行走

智能手表模型使用双腕设备数据进行训练，而智能手机模型测试阶段仅采用前袋与后袋手机数据（因其预期使用率最高且精度最佳）。我们采用五折交叉验证策略，将同一参与者的所有数据归入同一折，以最大化测试队列并防止数据泄露。

关键发现
综合结果表明，基于智能手表的方法在准确性、相关性和可靠性方面均表现优异，与智能手机估算性能相当（尽管智能手机模型的训练数据段约为智能手表的两倍）。

• 强效度与优异信度：在7万段步行数据中，智能手表对多数指标（包括步速、步长、摆动时间和站立时间）展现出强效度（皮尔逊相关系数r>0.80）和优异信度（组内相关系数ICC>0.80）。双支撑时间的ICC值略低但仍可接受（0.56–0.60），其95%置信区间较窄，印证了可靠性。
• 与智能手机估算性能相当：MAPE（见下图）和平均绝对误差（MAE）的定量比较显示，Pixel手表与Pixel手机在所有步态指标上均无显著差异（p>0.05）。这确立了智能手表作为精准步态分析的可行且高度可比对的平台。智能手表与智能手机模型均显著优于仅预测训练样本均值的简单估算器。
• 用户身高的作用：消融实验证实，提供用户身高能显著提升智能手表估算步速和步长的准确性，凸显了用户身高在腕部步态指标估算中的独特重要性。

影响与未来方向
这些发现是确立普及化的腕戴智能手表作为精准可靠步态健康追踪基础技术的重要一步。通过将综合步态分析从实验室移至手腕，我们能够实现：

• 持续可及的追踪：在传统临床和实验室环境之外纵向监测步态指标
• 早期检测与预防：为疾病早期发现、跌倒风险评估及个性化康复规划提供更大潜力

智能手表为健康追踪提供了实用且稳定的平台，克服了智能手机的放置限制。我们将继续完善和扩展指标体系，以最大限度发挥智能手表在主动健康追踪与建议中的效用。

致谢
以下研究人员为本研究作出贡献：Amir B. Farjadian、Shun Liao

英文来源：

Unlocking health insights: Estimating advanced walking metrics with smartwatches
January 15, 2026
Amir Farjadian, Research Scientist, and Ming-Zher Poh, Staff Research Scientist, Google
We verified that smartwatches serve as a highly reliable platform for estimating spatio-temporal gait metrics through a large-scale validation study.
Quick links
Gait metrics — measures like walking speed, step length, and double support time (i.e., the proportion of gait cycle when both feet are on the ground) — are known to be vital biomarkers for assessing a person’s overall health, risk of falling, and progression of neurological or musculoskeletal conditions. Analyzing how a person walks, known as gait analysis, offers valuable, non-invasive insights into general well-being, injuries, and health concerns.
Historically, measuring gait required expensive, specialized laboratory equipment, making continuous tracking impractical. While smartphones now offer a portable alternative using their embedded inertial measurement units (IMUs), they demand precise placement — such as a thigh pocket or belt — for the most accurate results. In contrast, smartwatches are worn on the wrist in a fixed location. This provides a much more practical and consistent platform for continuous tracking, even expanding the tracking window to phone-less scenarios like walking around the house.
Despite this crucial logistical advantage, smartwatches have historically lagged behind smartphones in comprehensive gait metric evaluation. In our work, "Smartwatch-Based Walking Metrics Estimation", we sought to bridge this gap. We demonstrated that consumer smartwatches are a highly viable, accurate, and reliable platform for estimating a comprehensive suite of spatio-temporal gait metrics, with performance comparable to smartphone-based methods.
A deep learning approach for the wrist
To achieve this, we developed a multi-output (i.e., multi-head) deep learning model built on a temporal convolutional network (TCN) architecture identical for both smartwatch and smartphone data. This multi-head model is a key differentiator from prior TCN-based approaches, which often only provide temporal events (like contact points) that require drift-prone integration for spatial metrics like step length and gait speed. Our model, in contrast, directly estimates all spatio-temporal gait metrics.
Our model takes two key inputs, user height (a single scalar demographic input) and raw IMU signals, which include 3-axis accelerometer and 3-axis gyroscope data from a single on-wrist Pixel Watch at 50 Hz sampling frequency. The model architecture extracts embeddings from the IMU sensor input, which are then concatenated with the demographic data before the final prediction layers. The multi-head model output then directly estimates a comprehensive suite of measures, including bilateral metrics, one head each for gait speed and double support time, and unilateral metrics, two heads each (one for left and one for right foot) for step length, swing time, and stance time. Definitions for each of these metrics are defined as:

• Gait speed: Distance an individual travels divided by the time taken (in cm/s)
• Double support time: Proportion of gait cycle when both feet are on the ground (in %)
• Step length (unilateral): Distance from the initial contact of one foot to the initial contact of the other foot (in cm)
• Swing time (unilateral): Duration within the gait cycle when the foot is not in contact with the ground (in ms)
• Stance time (unilateral): Duration within the gait cycle when the foot is in contact with the ground (in ms)
  For data segmentation, we used 5-second windows with a 1-second overlap. We utilized mean absolute percentage error (MAPE) for the loss function, which uniquely optimizes for the relative accuracy across all multi-unit outputs (e.g., step length in cm, double support time in ms).
  Rigorous validation in a large-scale study
  To rigorously evaluate the model, we conducted a large-scale validation study featuring a large cohort of 246 participants and approximately 70,000 walking segments. Participants were screened to be over 18, not using assistive devices, and without balance- or gait-affecting conditions. Data was collected from two international cohorts: Google in Mountain View, California and Kyoto University in Japan.
  For the reference (ground truth) measurements, we used a lab-grade Zeno Gait Walkway system. Participants were outfitted with a Pixel Watch 1 on each wrist and four Pixel 6 phones placed in the front pocket, back pocket, backpack, and a cross-body bag.
  The study protocol included a diverse range of walking patterns to ensure comprehensive evaluation:
• Six minute walk test (6MWT): Complete loops along the track at a self-paced speed
• Fast pace walking: Walk at a comfortably fast but steady pace
• Mild and moderate asymmetry: Walk self-paced while wearing a hinged knee brace locked into specific flexion/extension angles
  The smartwatch model was trained using data from both wrist-worn devices, while the smartphone model's testing phases exclusively utilized data from front and back pocket phone placements, given their expected prevalence and highest accuracy. We employed a five-fold cross-validation strategy to maximize the test cohort and prevent data leakage by assigning all data from a single participant to a single split.
  Key findings
  The results collectively demonstrated the accuracy, correlation, and reliability of the smartwatch-based method, showing comparable performance to smartphone estimates, despite the smartphone model being trained with approximately two times more data segments.
• Strong validity and excellent reliability: Across the 70,000 walking segments, smartwatch estimates demonstrated strong validity (Pearson r >0.80) and excellent reliability (intraclass correlation coefficient (ICC) >0.80) for most metrics, including gait speed, step length, swing time, and stance time. Double support time showed moderately lower but acceptable ICCs (0.56−0.60) with narrow 95% confidence intervals, underscoring reliability.
• Comparable to Smartphone Estimates: A quantitative comparison of the MAPE (see below) and mean absolute error (MAE) showed non-significant differences (p >0.05) between the Pixel Watch and Pixel phone across all measured gait metrics. This establishes the smartwatch as a viable and highly comparable platform for accurate gait analysis. Both the smartwatch and smartphone models significantly outperformed a naïve estimator, which merely predicted the mean of the training samples.
• The role of user height: An ablation study confirmed that providing user height significantly improved the smartwatch's accuracy for estimating gait speed and step length, which highlights the distinct importance of user height for wrist-based gait metric estimation.
  Impact and future directions
  These findings are a major step in establishing the ubiquitous on-wrist smartwatch as a foundational technology for accurate and reliable gait-based health tracking. By bringing comprehensive gait analysis out of the lab and onto the wrist, we can enable:
• Continuous and accessible tracking: Longitudinal monitoring of gait metrics outside traditional clinical and laboratory settings
• Early detection and prevention: Greater potential for the early detection of disease, fall risk, and personalized rehabilitation planning.
  The smartwatch offers a practical and consistent platform for health tracking that overcomes the placement issues associated with smartphones. Our continued work will explore refining and expanding the suite of metrics to maximize the utility of smartwatches in proactive health tracking and recommendations.
  Acknowledgements
  The following researchers contributed to this work: Amir B. Farjadian, Shun Liao