Gait speed, often considered the sixth vital sign, is a meaningful health indicator that is routinely assessed within clinical and research settings [1-4]. As a biomarker, gait speed is an integrated and reproducible measure of mobility that helps to identify the risk of falls, disability, hospitalization, and even mortality [5,6]. It is also responsive to intervention and is often used to evaluate the effectiveness and progression of rehabilitation programming [3,7]. Within clinical settings, gait speed is most commonly measured by using a stopwatch to record the time taken to walk a short distance [8]. This approach, while validated, requires another person’s assistance and is often inaccurate due to human error or bias. Within the laboratory, gait speed is typically measured using motion capture systems or instrumented walkways. This approach increases measurement accuracy yet requires trained personnel and expensive equipment, limiting its accessibility and feasibility for frequent, longitudinal monitoring. Moreover, neither of these clinical or laboratory-based approaches is well-suited for monitoring changes over time via higher-frequency assessments, and neither approach affords the assessment of gait speed assessment under unsupervised conditions during daily life activities. There is thus a need to develop new methods for the low-cost, easy, and accurate measurement of gait speed for use within both clinical and remote, unsupervised settings.

Smartphones, now widely used by younger and older adults and equipped with a high-quality inertial measurement unit (IMU), present a viable and convenient opportunity to objectively measure gait speed outside of laboratory and clinical settings. Previous studies have demonstrated that gait speed can be accurately estimated using multiple IMUs attached to various parts of the body (eg, the trunk and ankles) [9-11]. However, this approach requires specialized equipment that may be difficult to don and doff. Alternatively, studies have attempted to estimate gait speed using a single, research-grade IMU attached to the trunk of the body. In this case, the inverted pendulum model was used to determine gait speed by estimating stride distance and dividing it by stride time, determined by acceleration peaks induced by consecutive heel strikes [12-15]. This approach, while promising, still requires trained personnel for equipment setup and may introduce bias in gait speed estimation, especially at a faster or slower gait speed [13-15]. In this study, we examined the validity and reliability of deriving gait speed from a smartphone IMU with the phone being carried in the individual’s front pants pocket when walking.

This study aimed to validate and test the reliability of a smartphone IMU–based gait speed measurement placed in the user’s pants pocket in both healthy young and older adults during normal and dual-task walking. The validity of the smartphone-based approach was determined by comparing the estimated step length and gait speed to measurements taken by gold standard instrumentation. Based on initial results, we further examined the use of improving gait speed estimations using a coefficient relative to the estimation of step length as determined by the smartphone. The test-retest reliability of adjusted and unadjusted estimations was examined within both laboratory and unsupervised, real-life home settings. This study is expected to provide valuable insights applicable to a wide range of clinical and everyday gait monitoring scenarios.

This study indicated that using a smartphone app placed in the front pocket of an individual’s pants or shorts can produce valid estimates of gait speed under both normal and dual-task walking conditions in healthy younger and older adults. Moreover, we demonstrated that the accuracy of gait speed estimation using the inverted pendulum model approach can be improved by adjusting the estimated values with coefficients determined by the gold standard measurement of step length. This improved smartphone app–based approach provided high test-retest reliability within a supervised laboratory environment and within the unsupervised home setting.

The current results revealed that the pendulum model approach to calculating gait speed from a single IMU placed in the pocket overestimated step length and therefore gait speed if the step length derived from the GAITRite mat was greater than 0.8 m, yet underestimated both values if the step length was less than 0.5 m. Through visual inspection of published work, it appears that this observed bias in step length estimation is common, even when the IMU is secured to the participant’s trunk [13,15]. To our knowledge, however, no one has reported this observation, and no adjustment has been made to correct the bias.

Our work suggests that a simple adjustment of step length for those steps that are relatively short, or relatively long, removes the observed bias and improves the accuracy of gait speed estimation. The current results suggested that the original estimated step length from the smartphone app between 0.5 and 0.8 m may not need to be adjusted as its coefficient was close to 1. Comparing our smartphone app with gold standard measurements, it is evident that in scenarios involving normal or dual-task walking, the app maintains robust performance with small bias across younger and older groups, different walking conditions, and different testing settings (laboratory and home). This implies that the described method can be used accurately within numerous applications. The simple inverted pendulum model requires only 2 pieces of information, namely, the participant’s leg length and the change in height of the smartphone’s vertical position during each step (from heel strike to contralateral heel strike). We suspect that several factors may contribute to the observed bias. There may be nonlinear relationships between step length and the combinations of changes in the height of the smartphone’s vertical position and leg length. This could indicate inherent limitations in the inverted pendulum model. Exploring these factors further may thus offer insights into optimizing the model for different IMU locations and gait patterns, enhancing the applicability of our method across diverse populations and conditions.

The results from this study not only reinforce the reliability of the approach to estimate gait speed under different walking conditions in ambulatory older adults without overt disease but also suggest its applicability to all IMU data. Good to excellent test-retest reliability of results was observed across younger and older groups, different walking conditions (normal walking and dual-task walking), and different testing settings (laboratory and home). This implies that the described method can be used effectively within numerous applications. Moreover, the test-retest reliability observed in this study was noticeably higher than a previous report that used the pendulum model without adjustment [15]. Incorporating a wide range of ages from young to old in this study allowed us to encompass a broader spectrum of gait speeds and step lengths, which enhanced the generalizability of our findings. The observed consistency in the adjustment coefficients across these age groups suggests that the pendulum model approach we used is robust and applicable across a diverse age demographic. This inclusivity in our research approach enriches its relevance and widens its potential impact, thus making our findings more applicable in real-world scenarios where age-related variations in gait are common.

The coefficients established from our cohort may be applicable to other data sets where the populations and the settings of the devices (smartphone), including sampling rate, are similar; yet, the coefficients may need to be fine-tuned for different populations and devices, especially when a gold standard measurement is available. Our coefficients performed well in our data set, which involved a wide range of step length and gait speed in both healthy young and older populations. In terms of future application to other data sets, this method holds significant potential for various areas. For data sets where a gold standard method is unavailable, our coefficients are worth trying to adjust the original estimated value. If the population (ie, healthy young and healthy older adults) and tool (ie, a smartphone app in the pants pocket) are similar to that tested and used in this study, implementing this type of adjustment is expected to improve the accuracy and reliability of the estimates. For data sets where a gold standard is available, researchers are recommended to develop their own coefficients based on the unique characteristics of each data set, thereby improving the method’s precision and applicability to specific contexts.

Further research is required to determine the generalizability of our method to broader populations, especially in cases where a gold standard method is not available. The original step length estimations in this study were classified into 3 categories, which may decrease the test-retest reliability of those step length data close to the margin of categories. To address this, larger data sets are needed to establish a continuous adjustment approach without the need to classify the data into categories. Additionally, refining the inverted pendulum model to better accommodate the nonlinear relationship between step length and smartphone vertical displacement could enhance accuracy. The applicability of the method to individuals with abnormal gait patterns should also be explored. Despite these limitations, this study provided evidence that gait speed can be accurately and reliably estimated with minimum information (leg length) in young and older adults during normal and dual-task walking based upon a single IMU-embedded smartphone placed in the pocket.