Background: Estimation of temporospatial clinical features of gait (CFs), such as step count and length, step duration, step frequency, gait speed and distance traveled is an important component of community-based mobility evaluation using wearable accelerometers. However, challenges arising from device complexity and availability, cost and analytical methodology have limited widespread application of such tools. Research Question: Can accelerometer data from commercially-available smartphones be used to extract gait CFs across a broad range of attainable gait velocities in children with Duchenne muscular dystrophy (DMD) and typically developing controls (TDs) using machine learning (ML)-based methods Methods: Fifteen children with DMD and 15 TDs underwent supervised clinical testing across a range of gait speeds using 10 or 25m run/walk (10MRW, 25MRW), 100m run/walk (100MRW), 6-minute walk (6MWT) and free-walk (FW) evaluations while wearing a mobile phone-based accelerometer at the waist near the body's center of mass. Gait CFs were extracted from the accelerometer data using a multi-step machine learning-based process and results were compared to ground-truth observation data. Results: Model predictions vs. observed values for step counts, distance traveled, and step length showed a strong correlation (Pearson's r = -0.9929 to 0.9986, p<0.0001). The estimates demonstrated a mean (SD) percentage error of 1.49% (7.04%) for step counts, 1.18% (9.91%) for distance traveled, and 0.37% (7.52%) for step length compared to ground truth observations for the combined 6MWT, 100MRW, and FW tasks. Significance: The study findings indicate that a single accelerometer placed near the body's center of mass can accurately measure CFs across different gait speeds in both TD and DMD peers, suggesting that there is potential for accurately measuring CFs in the community with consumer-level smartphones.

翻译：背景：利用可穿戴加速度计对步态时空临床特征（CFs）进行估计，如步数与步长、步态周期时长、步频、步速及行进距离，是基于社区移动能力评估的重要组成部分。然而，设备复杂性、可用性、成本及分析方法带来的挑战限制了此类工具的广泛应用。研究问题：能否通过商用智能手机的加速度计数据，结合基于机器学习（ML）的方法，在杜氏肌营养不良症（DMD）患儿及典型发育对照（TDs）可达到的广泛步态速度范围内提取步态CFs？方法：15名DMD患儿及15名TDs在监督下完成临床测试，涵盖多种步速，包括10米或25米跑/走（10MRW、25MRW）、100米跑/走（100MRW）、6分钟步行（6MWT）及自由步行（FW）评估，测试时在腰部（接近身体质心位置）佩戴基于手机的加速度计。通过多步机器学习流程从加速度计数据中提取步态CFs，并将结果与地面实况观测数据进行比较。结果：模型对步数、行进距离及步长的预测值与观测值呈现强相关性（皮尔逊r = -0.9929至0.9986，p<0.0001）。在合并6MWT、100MRW及FW任务中，与地面实况观测相比，步数估计的均值（标准差）百分比误差为1.49%（7.04%），行进距离为1.18%（9.91%），步长为0.37%（7.52%）。意义：研究结果表明，在接近身体质心位置放置单个加速度计可准确测量TDs及DMD同龄人在不同步速下的步态CFs，提示使用消费级智能手机在社区环境中实现步态CFs的精确测量具有潜力。