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.
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.
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.
Single group
Gait analysis via vicon- and app-system
Diagnostic testParticipants will undergo standardized gait assessment under controlled laboratory conditions. Each participant will carry two study smartphones, one iOS and one Android device, positioned in trouser pockets while walking on a Vicon optical motion-capture gait-analysis setup. Simultaneous recordings of smartphone inertial sensor data and Vicon motion-capture data will be obtained during repeated two-minute walking trials. The procedure will first be performed while participants wear their own trousers and then repeated using standardized trousers with defined pocket positions at thigh level. The intervention is non-invasive and purely observational; no therapeutic intervention or clinical decision-making is involved. The collected data will be used to compare AI-based smartphone-derived gait parameters with the Vicon reference standard.
Intraclass correlation coefficient between smartphone-derived and Vicon-derived gait parameters
Intraclass correlation coefficients will be calculated to assess agreement between smartphone-derived gait parameters and simultaneously recorded Vicon-derived gait parameters. Parameters will include gait speed, step length, cadence, step time, double support time, gait asymmetry, lower-extremity kinematic angle parameters, and knee joint range of motion.
Mean absolute error and root mean square error between smartphone-derived and Vicon-derived gait parameters
Mean absolute error and root mean square error will be calculated for smartphone-derived gait parameters compared with simultaneously recorded Vicon-derived reference values. Parameters will include gait speed, step length, cadence, step time, double support time, gait asymmetry, lower-extremity kinematic angle parameters, and knee joint range of motion.
Difference in agreement and measurement error between own trousers and standardized trousers
Agreement and measurement error will be compared between the own-trouser condition and the standardized-trouser condition using intraclass correlation coefficients, mean absolute error, root mean square error, and Bland-Altman mean paired differences.
Mean paired difference and 95% limits of agreement between smartphone-derived and Vicon-derived gait parameters
Bland-Altman analyses will be used to quantify systematic bias and 95% limits of agreement between smartphone-derived and Vicon-derived gait parameters. The mean paired difference will be reported for each gait parameter.
Intraclass correlation coefficient for test-retest reliability across repeated walking trials
Test-retest reliability of smartphone-derived gait parameters will be assessed across repeated walking trials using intraclass correlation coefficients. Parameters will include gait speed, step length, cadence, step time, double support time, gait asymmetry, lower-extremity kinematic angle parameters, and knee joint range of motion
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