Acceleration Gait Measures as Proxies for Motor Skill of Walking: A Narrative Review

In adults 65 years or older, falls or other neuromotor dysfunctions are often framed as walking-related declines in motor skill; the frequent occurrence of such decline in walking-related motor skill motivates the need for an improved understanding of the motor skill of walking. Simple gait measurements, such as speed, do not provide adequate information about the quality of the body motion’s translation during walking. Gait measures from accelerometers can enrich measurements of walking and motor performance. This review article will categorize the aspects of the motor skill of walking and review how trunk-acceleration gait measures during walking can be mapped to motor skill aspects, satisfying a clinical need to understand how well accelerometer measures assess gait. We will clarify how to leverage more complicated acceleration measures to make accurate motor skill decline predictions, thus furthering fall research in older adults

Acceleration Signals In Determining Gait-Related Difficulties And The Motor Skill Of Walking In Older Adults

In adults 65 years or older, falls or other neuromotor dysfunctions are often framed as walking-related declines in motor skill; the frequent occurrence of such decline in walking-related motor skill motivates the need for an improved understanding of motor skill in walking. Simple gait measurements, such as speed, do not provide adequate information about the quality of the translation of the body motion during walking. Furthermore, there is a great need in the clinical literature and clinical practice for more accurate measures of the loss of the motor skill of walking, so that clinical practice can provide better therapeutic interventions to improve the motor skill of walking. This dissertation suggests a consensus on what the motor skill of walking is and dissects it into seven interrelated characteristics and traits. Subsequently, we purport that these characteristics of the motor skill of walking cannot be represented by simple gait measurements or raw sensor measurements alone. Gait measures from accelerometers placed on the lower trunk, or trunk-acceleration gait measures, can enrich measurements of walking and motor performance. To support our claim, we will map these acceleration gait measures (AGMs) to the various aspects of the motor skill of walking. Additionally, influential AGMs will be elected through feature selection methods. Various machine learning algorithms ranging from logistic regression, non-linear regression, evolutionary algorithms, and ensemble methods will be used to make predictions on age-related gait-related difficulty outcomes (such as fall risk). Overall, we hope to find that the combination of high-fidelity artificial intelligence algorithms and acceleration gait measures derived from low-cost sensors can fulfill the severe and crucial need for the clinical measurement of the motor skill of walking in older adults

“You can tell by the way I use my walk.” Predicting the presence of cognitive load with gait measurements

Abstract Background There is considerable evidence that a person’s gait is affected by cognitive load. Research in this field has implications for understanding the relationship between motor control and neurological conditions in aging and clinical populations. Accordingly, this pilot study evaluates the cognitive load based on gait accelerometry measurements of the walking patterns of ten healthy individuals (18–35 years old). Methods Data points were collected using six triaxial accelerometer sensors and treadmill pressure reports. Stride and window extraction methods were used to process these data points and separate into statistical features. A binary classification was created by using logistic regression, support vector machine, random forest, and learning vector quantization to classify cognitive load vs. no cognitive load. Results Within and between subjects, a cognitive load was predicted with accuracy values ranged of 0.93–1 by all four models. Various feature selection methods demonstrated that only 2–20 variables could be used to achieve similar levels of accuracies. Conclusion Coupling sensors with machine learning algorithms to detect the most minute changes in gait patterns, most of which are too subtle to identify with the human eye, may have a remarkable impact on the potential to detect potential neuromotor illnesses and fall risks. In doing so, we can open a new window to human health and safety prevention