Biomechanical and Technical Comparison of AI-based Smartphone-derived Gait Parameters With the Gold Standard
试验速览
- 阶段
- 不适用
- 状态
- 尚未招募
- 发起方
- 入组人数
- 40
研究概览
简要总结
This monocentric prospective cohort study evaluates the technical agreement between artificial intelligence (AI)-based smartphone-derived gait parameters and an optical motion-capture system as the current technical gold standard for gait analysis. Wearables and smartphone-based inertial measurement units (IMUs) offer a scalable and low-threshold approach to assessing human gait mechanics outside specialized gait laboratories. However, before such approaches can be used reliably in clinical research or future clinical pathways, their technical validity and agreement with established reference systems need to be systematically quantified under controlled conditions.
The study will include 40 healthy adult volunteers without acute or chronic disorders of the lower extremities. Participants will be recruited among employees and students of the TUM School of Medicine and Health. After written informed consent, each participant will undergo standardized gait testing at the TUM Campus in the Olympiapark. During testing, participants will carry an iPhone and an Android smartphone in their trouser pockets while simultaneously being assessed with a Vicon optical motion-capture system. The walking test will consist of repeated two-minute walking trials. First, participants will walk wearing their own trousers. Subsequently, the measurements will be repeated while wearing standardized trousers with defined pocket positions at thigh level. All device and Vicon data will be recorded in parallel.
The primary objective is to evaluate the level of agreement between AI-based smartphone-derived gait parameters and Vicon-based gait analysis. Primary outcome measures include the intraclass correlation coefficient (ICC), Bland-Altman limits of agreement, mean absolute error (MAE), and root mean square error (RMSE) for key spatiotemporal and kinematic gait parameters. These parameters include gait speed, step length, cadence, step time, double support time, gait asymmetry, and lower-limb kinematic angle parameters, particularly knee range of motion. Secondary objectives include comparison between Android and iOS devices, assessment of test-retest reliability, evaluation of the influence of trouser type, and analysis of potential systematic bias.
The study is exploratory and non-invasive. It does not provide direct individual benefit to participants, but it is expected to generate relevant scientific and technical evidence regarding the accuracy, reproducibility, and limitations of smartphone-based gait analysis. The risks for participants are minimal and limited to ordinary walking-related discomfort, mild fatigue, a very low risk of stumbling, and rare minor skin irritation from motion-capture markers. No vulnerable groups will be included. Data will be anonymized and processed in accordance with the General Data Protection Regulation.
详细描述
Background and Rationale Quantitative gait analysis is an established method for assessing human movement, lower-limb function, mobility, and biomechanical performance. The current technical gold standard for precise gait analysis is optical motion capture, such as Vicon-based three-dimensional motion analysis. These systems provide high-resolution spatiotemporal and kinematic data, but their use is largely restricted to specialized gait laboratories because they require dedicated infrastructure, trained personnel, time-consuming preparation, and substantial financial resources. Consequently, gold-standard gait analysis is rarely available in routine clinical care, large-scale research settings, or home-based longitudinal monitoring.
In contrast, smartphones and wearable devices contain inertial measurement units, including accelerometers, gyroscopes, and magnetometers, which allow the recording of movement-related raw sensor data in a highly scalable and low-threshold manner. AI-based algorithms may enable automated extraction of clinically and biomechanically relevant gait parameters from such sensor data without the need for complex laboratory equipment. This creates the prospect of continuous or repeated gait assessment in clinical, ambulatory, or home-based environments.
However, before AI-based smartphone gait analysis can be interpreted as a valid technical alternative or complement to established laboratory-based systems, its agreement with the gold standard must be quantified under controlled experimental conditions. Key questions include whether smartphone-derived gait parameters correlate sufficiently with motion-capture-derived parameters, whether systematic measurement bias exists, how large the measurement error is, whether repeated measurements are reliable, and whether device platform or clothing-related factors influence measurement accuracy.
This study therefore investigates the technical comparability of AI-based smartphone-derived gait parameters with simultaneous Vicon optical motion-capture measurements in healthy adult volunteers. By quantifying agreement, measurement deviation, test-retest reliability, and potential sources of bias, the study aims to provide a technical foundation for future clinical and scientific use of smartphone-based gait assessment.
Study Objective The primary objective of this study is to evaluate the agreement between AI-based smartphone-derived gait parameters and gait parameters obtained from an optical Vicon motion-capture system.
研究设计
- 研究类型
- Interventional
- 分配方式
- Na
- 干预模型
- Single Group
- 主要目的
- Diagnostic
- 盲法
- None
入排标准
- 年龄范围
- 18 Years 至 —（Adult, Older Adult）
- 性别
- All
- 接受健康志愿者
- 是
入选标准
- •Adults aged 18 years or older. No known acute or chronic disorder or injury of the lower extremities. Ability to walk independently. Sufficient physical capacity to complete a standardized walking test. Ability to understand the study information and provide written informed consent. German-, English-, or Spanish-speaking.
排除标准
- •Acute injury or disease of the lower extremity. Chronic injury or disease of the lower extremity. Inability to walk independently. Relevant motor impairment or other major limitation affecting safe gait testing. Lack of capacity to provide informed consent. Inability to understand German, English, or Spanish. Insufficient physical capacity to perform the walking test. Any condition that, in the opinion of the study team, would impair participant safety or the validity of the gait measurements.
研究者
Christina Valle
Dr. med.
Technical University of Munich