Does external walking environment affect gait patterns?

Abstract

Abstract-The objective of this work is to develop an understanding of the relationship between mobility metrics obtained outside of the clinic or laboratory and the context of the external environment. Ten subjects walked with an inertial sensor on each shank and a wearable camera around their neck. They were taken on a thirty minute walk in which they mobilized over the following conditions; normal path, busy hallway, rough ground, blind folded and on a hill. Stride time, stride time variability, stance time and peak shank rotation rate during swing were calculated using previously published algorithms. Stride time was significantly different between several of the conditions. Technological advances mean that gait variables can now be captured as patients go about their daily lives. The results of this study show that the external environment has a significant impact on the quality of gait metrics. Thus, context of external walking environment is an important consideration when analyzing ambulatory gait metrics from the unsupervised home and community setting. I. INTRODUCTIONFalling is a common occurrence in older adults and is a leading cause of serious injury, loss of independence, and nursing-home admission [1]. Prevention of falls results in prevention of injury and maintenance of independent living [2]. It is important to identify elderly persons who are particularly at risk of suffering a fall. Identification of high risk individuals allows fall prevention interventions to be directed appropriately.Research has shown that metrics from supervised mobility assessments can be used to identify elderly patients who may be at an increased risk of falling. It has been shown that stride time variability from a supervised walking trial can be used to predict risk of falling [3]. More recently it has been shown that wearable sensor metrics from a timed up and go (TUG) test can be used to predict risk of falling [4]. Such tests are very useful because they can identify individuals who are at an increased risk of falling. Such individuals should be directed towards interventions to reduce their risk of falling. A. Inomata is with Fujitsu Laboratories Japan.A limitation of the methods presented above [3,4] is that these tests require clinical supervision to be performed. With advancing technology is it now possible to use wearable sensors to monitor mobility as people go about their daily lives, without the need for a visit to the hospital or clinic [5]. Utilization of mobility data from daily life to identify falls risk would mean that a much larger group of elderly individuals could have their falls risk monitored compared to using clinically supervised testing alone. An added benefit would be that real world mobility data would be used to assess falls risk, as opposed to mobility data from the controlled supervised clinical environment.Lesson's learned regarding mobility metrics and their relationship to falls risk from the supervised environment cannot be directly applied to an unsupervised environment. It is not ...

Continuous Digital Monitoring of Walking Speed in Frail Elderly Patients: Noninterventional Validation Study and Longitudinal Clinical Trial

“…Variability observed in real-world behaviors is heavily influenced by external factors [23]; for example, gait speeds observed in this study may be influenced by changing footwear or weather. Research is now beginning to show how overlaying external contextual information, for example, location [24], onto objectively captured sensor data and patient-reported outcome data can help explain some of the observed variations and enable even more meaningful comparisons.…”

The Impact of Environment on Gait Assessment: Considerations from Real-World Gait Analysis in Dementia Subtypes

“…The strongest correlations between lab and real-world gait metrics were also found in the longer walking bouts, suggesting the algorithm performs better in continuous walking—which only accounts for a small proportion of total data collected. As such, we need to gain a better understanding about how environmental context impacts gait, beyond considering length of walking bouts [ 36 ]. This could be done through initial validation with wearable cameras, and less obtrusively with companion applications such as mobile GPS or assessment of the walkability of the participants’ usual environment [ 37 ].…”

Gait Asymmetry Post-Stroke: Determining Valid and Reliable Methods Using a Single Accelerometer Located on the Trunk

“…These results provide evidence that asymmetry can accurately and reliably be calculated using a single accelerometer. Although much work is needed for accelerometers to be routinely adopted [42,43], these results give evidence that asymmetry can be objectively quantified using a tool applicable for many purposes. Consequently, the variables tested here may then act as a digital biomarker to quantify the impact of targeted interventions proposed to improve gait timing mechanisms and gait asymmetry (e.g., auditory rhythmical cueing) [44].…”