Analysis of dual-task elderly gait using wearable plantar-pressure insoles and accelerometer

Foot pressure-based analysis of gait while using a smartphone

BACKGROUND

The number of incidents related to walking while using smartphones is rising. However, it is not clear how smartphone usage might affect a gait pattern in terms of the foot pressure, and this may address the mechanism leading to incidents while using smartphones.

RESEARCH QUESTION

How do the characteristics of walking while using a smartphone affect foot pressure patterns?

METHODS

In this cross-sectional study, we recruited 40 healthy young participants and investigated the walking speed, step length, coefficient of variance of the walking cycle (CV), anteroposterior length of the center of pressure (COP) trajectory (%Long), partial foot pressure ratios (% partial foot pressure [%PFP]), and COP existence time (COPexT) under the following four conditions: normal walking, screen gazing, while using social networking services (SNS), and while using a cognitive application. Parameters were compared among the four conditions using a repeated-measures ANOVA. Further, according to the presence or absence of an incident history (e.g. stumbles, collisions), participants were divided into either the incident or non-incident group. Parameters were compared between the two groups using a two-way repeated-measures ANOVA.

RESULTS

Under the SNS and cognitive application conditions, the walking speed, step length, %Long, %PFP, and COPexT in the heel were significantly lower, and the CV and %PFP in the metatarsal region were higher than those under normal walking or screen gazing. %PFP in the heel and metatarsal regions showed a significant group-by-condition interaction; the incident group had lower %PFP in the heel region and higher %PFP in the metatarsal region than the non-incident group.

SIGNIFICANCE

These findings indicate a trend of loading more pressure on the forefoot than on the heel. This pattern was markedly evident in individuals with a history of incidents related to the smartphone usage and may be one of the factors causing stumbles and collisions.

Motoric Cognitive Risk Syndrome: Symptoms, Pathology, Diagnosis, and Recovery

The motoric cognitive risk (MCR) syndrome is a pre-dementia condition, marked by the enhanced risk for Alzheimer's disease (AD) and vascular dementia, together with falls, disability, and abnormal movements. The research studies revealed the distinct neurological and non-neurological clinical gait irregularities during dementia and accelerated functional decline, such as postural and balance impairments, memory loss, cognitive failure, and metabolic dysfunctions. The disabling characteristics of MCR comprise altered afferent sensory and efferent motor responses, together with disrupted visual, vestibular, and proprioceptive components. The pathological basis of MCR relates with the frontal lacunar infarcts, white matter hyperintensity (WMH), gray matter atrophy in the pre-motor and pre-frontal cortex, abnormal cholinergic functioning, inflammatory responses, and genetic factors. Further, cerebrovascular lesions and cardiovascular disorders exacerbate the disease pathology. The diagnosis of MCR is carried out through neuropsychological tests, biomarker assays, imaging studies, questionnaire-based evaluation, and motor function tests, including walking speed, dual-task gait tests, and ambulation ability. Recovery from MCR may include cognitive, physical, and social activities, exercise, diet, nutritional supplements, symptomatic drug treatment, and lifestyle habits that restrict the disease progression. Psychotherapeutic counseling, anti-depressants, and vitamins may support motor and cognitive improvement, primarily through the restorative pathways. However, an in-depth understanding of the association of immobility, dementia, and cognitive stress with MCR requires additional clinical and pre-clinical studies. They may have a significant contribution in reducing MCR syndrome and the risk for dementia. Overall, the current review informs the vital connection between gait performance and cognition in MCR and highlights the usefulness of future research in the discernment and treatment of dementiating illness.

Technology-Enabled Assessment of Functional Health

The maturation of pervasive computing technologies has dramatically altered the face of healthcare. With the introduction of mobile devices, body area networks, and embedded computing systems, care providers can use continuous, ecologically valid information to overcome geographic and temporal barriers and thus provide more effective and timely health assessments. In this paper, we review recent technological developments that can be harnessed to replicate, enhance, or create methods for assessment of functional performance. Enabling technologies in wearable sensors, ambient sensors, mobile technologies, and virtual reality make it possible to quantify real-time functional performance and changes in cognitive health. These technologies, their uses for functional health assessment, and their challenges for adoption are presented in this paper.

Dual-Task Elderly Gait of Prospective Fallers and Non-Fallers: A Wearable-Sensor Based Analysis

Wearable sensors could facilitate point of care, clinically feasible assessments of dynamic stability and associated fall risk through an assessment of single-task (ST) and dual-task (DT) walking. This study investigated gait changes between ST and DT walking and between older adult prospective fallers and non-fallers. The results were compared to a study based on retrospective fall occurrence. Seventy-five individuals (75.2 ± 6.6 years; 47 non-fallers, 28 fallers; 6 month prospective fall occurrence) walked 7.62 m under ST and DT conditions while wearing pressure-sensing insoles and accelerometers at the head, pelvis, and on both shanks. DT-induced gait changes included changes in temporal measures, centre of pressure (CoP) path stance deviations and coefficient of variation, acceleration descriptive statistics, Fast Fourier Transform (FFT) first quartile, ratio of even to odd harmonics, and maximum Lyapunov exponent. Compared to non-fallers, prospective fallers had significantly lower DT anterior–posterior CoP path stance coefficient of variation, DT head anterior–posterior FFT first quartile, ST left shank medial–lateral FFT first quartile, and ST right shank superior maximum acceleration. DT-induced gait changes were consistent regardless of faller status or when the fall occurred (retrospective or prospective). Gait differences between fallers and non-fallers were dependent on retrospective or prospective faller identification.

A Review on Accelerometry-Based Gait Analysis and Emerging Clinical Applications

Gait analysis continues to be an important technique for many clinical applications to diagnose and monitor certain diseases. Many mental and physical abnormalities cause measurable differences in a person's gait. Gait analysis has applications in sport, computer games, physical rehabilitation, clinical assessment, surveillance, human recognition, modeling, and many other fields. There are established methods using various sensors for gait analysis, of which accelerometers are one of the most often employed. Accelerometer sensors are generally more user friendly and less invasive. In this paper, we review research regarding accelerometer sensors used for gait analysis with particular focus on clinical applications. We provide a brief introduction to accelerometer theory followed by other popular sensing technologies. Commonly used gait phases and parameters are enumerated. The details of selecting the papers for review are provided. We also review several gait analysis software. Then we provide an extensive report of accelerometry-based gait analysis systems and applications, with additional emphasis on trunk accelerometry. We conclude this review with future research directions.

Analysis of dual-task elderly gait in fallers and non-fallers using wearable sensors

Dual-task (DT) gait involves walking while simultaneously performing an attention-demanding task and can be used to identify impaired gait or executive function in older adults. Advancment is needed in techniques that quantify the influence of dual tasking to improve predictive and diagnostic potential. This study investigated the viability of wearable sensor measures to identify DT gait changes in older adults and distinguish between elderly fallers and non-fallers. A convenience sample of 100 older individuals (75.5±6.7 years; 76 non-fallers, 24 fallers based on 6 month retrospective fall occurrence) walked 7.62m under single-task (ST) and DT conditions while wearing pressure-sensing insoles and tri-axial accelerometers at the head, pelvis, and left and right shanks. Differences between ST and DT gait were identified for temporal measures, acceleration descriptive statistics, Fast Fourier Transform (FFT) quartiles, ratio of even to odd harmonics, center of pressure (CoP) stance path coefficient of variation, and deviations to expected CoP stance path. Increased posterior CoP stance path deviations, increased coefficient of variation, decreased FFT quartiles, and decreased ratio of even to odd harmonics suggested increased DT gait variability. Decreased gait velocity and decreased acceleration standard deviations (SD) at the pelvis and shanks could represent compensatory gait strategies that maintain stability. Differences in acceleration between fallers and non-fallers in head posterior SD and pelvis AP ratio of even to odd harmonics during ST, and pelvis vertical maximum Lyapunov exponent during DT gait were identified. Wearable-sensor-based DT gait assessments could be used in point-of-care environments to identify gait deficits.