Wearable Inertial Measurement Units for Assessing Gait in Real-World Environments

Abstract

Background: Walking patterns can provide important indications of a person's health status and be beneficial in the early diagnosis of individuals with a potential walking disorder. For appropriate gait analysis, it is critical that natural functional walking characteristics are captured, rather than those experienced in artificial or observed settings. To better understand the extent to which setting influences gait patterns, and particularly whether observation plays a varying role on subjects of different ages, the current study investigates to what extent people walk differently in lab versus real-world environments and whether age dependencies exist. Methods: The walking patterns of 20 young and 20 elderly healthy subjects were recorded with five wearable inertial measurement units (ZurichMOVE sensors) attached to both ankles, both wrists and the chest. An automated detection process based on dynamic time warping was developed to efficiently identify the relevant sequences. From the ZurichMOVE recordings, 15 spatio-temporal gait parameters were extracted, analyzed and compared between motion patterns captured in a controlled lab environment (10 m walking test) and the non-controlled ecologically valid real-world environment (72 h recording) in both groups. Results: Several parameters (Cluster A) showed significant differences between the two environments for both groups, including an increased outward foot rotation, step width, number of steps per 180 • turn, stance to swing ratio, and cycle time deviation in the real-world. A number of parameters (Cluster B) showed only significant differences between the two environments for elderly subjects, including a decreased gait velocity (p = 0.0072), decreased cadence (p = 0.0051) and increased cycle time (p = 0.0051) in real-world settings. Importantly, the real-world environment increased the differences in several parameters between the young and elderly groups. Conclusion: Elderly test subjects walked differently in controlled lab settings compared to their real-world environments, which indicates the need to better understand

How We Found Our IMU: Guidelines to IMU Selection and a Comparison of Seven IMUs for Pervasive Healthcare Applications

“…Stride-to-stride variability of stride time, stride length, as well as swing time are examples of valid indicators for falls risk [18][19][20]. Recent studies showed that the gait characteristics in controlled lab settings and daily life settings are different [21,22]. Therefore, being able to monitor gait patterns in an unobtrusive and cost-effective manner in real-life situations is of great interest to the goal of pervasive healthcare.…”

The Effect of Different Turn Speeds on Whole-Body Coordination in Younger and Older Healthy Adults

“…The stepping actions during a turn are critical as they may be an indication of instability and loss of coordination. Slow turning has been found to be associated with smaller and more frequent steps; characteristics that are also more common in older adults [ 13 , 14 ]. During turning in both a standing or walking turn, older adults often turn more slowly and with more rigid trunk movements, which may represent a strategy to compensate for actual or perceived instability [ 12 , 13 , 14 ].…”

Template-Based Recognition of Human Locomotion in IMU Sensor Data Using Dynamic Time Warping

“…Thus, no external validity could be demonstrated and there is a risk that the algorithm shows increased recognition rates when applied to another dataset. Renggli et al demonstrated that real-world conditions increase the differences in certain gait parameters between young and old populations [ 18 ]. However, the aim was to develop, validate and test the algorithm.…”

“…The test showed that DTW can successfully extract time information of the transitions and turns and thereby demonstrated that DTW can be suitable in activity recognition. Renggli et al used a template matching approach based on dynamic time warping to identify steps for gait assessments in laboratory and real-world environments [ 18 ]. Dot et al suggested a template-based approach using DTW and a single step template of gyroscope data for step recognition in younger adults [ 19 ].…”

“…This is mainly due to: the spatial limitations of the laboratory environment; the scripted activities; and behavioral differences due to the awareness of being observed, known as the Hawthorne effect [ 21 ]. Renggli et al demonstrated that especially older adults walk differently in laboratory conditions compared to real-world conditions [ 18 ]. Furthermore, real-world conditions increased the difference in several gait parameters between younger and older adults.…”