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

Howcroft, Jennifer; Lemaire, Edward D.; Kofman, Jonathan; McIlroy, William E.

doi:10.3390/s18041275

Cited by 31 publications

(33 citation statements)

References 37 publications

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“…To obtain the data, the included studies used between one and five inertial sensors. That were mainly located close to the centre of the body at the lower back [40,[42][43][44][45][46][47][48][49][50][51][52][53][54] or legs [43,50,[55][56][57][58] of the participants. Less frequently used sensor locations were chest [19,[58][59][60], pelvis [41,56,57], waist [61,62], foot [19,45,46], head [56,57] and wrist [63].…”

Section: Use Of Sensors To Assess Fall Riskmentioning

confidence: 99%

Sensor-based fall risk assessment in older adults with or without cognitive impairment: a systematic review

Bezold

Krell‐Roesch

Eckert

et al. 2021

Eur Rev Aging Phys Act

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Background Higher age and cognitive impairment are associated with a higher risk of falling. Wearable sensor technology may be useful in objectively assessing motor fall risk factors to improve physical exercise interventions for fall prevention. This systematic review aims at providing an updated overview of the current research on wearable sensors for fall risk assessment in older adults with or without cognitive impairment. Therefore, we addressed two specific research questions: 1) Can wearable sensors provide accurate data on motor performance that may be used to assess risk of falling, e.g., by distinguishing between faller and non-faller in a sample of older adults with or without cognitive impairment?; and 2) Which practical recommendations can be given for the application of sensor-based fall risk assessment in individuals with CI? A systematic literature search (July 2019, update July 2020) was conducted using PubMed, Scopus and Web of Science databases. Community-based studies or studies conducted in a geriatric setting that examine fall risk factors in older adults (aged ≥60 years) with or without cognitive impairment were included. Predefined inclusion criteria yielded 16 cross-sectional, 10 prospective and 2 studies with a mixed design. Results Overall, sensor-based data was mainly collected during walking tests in a lab setting. The main sensor location was the lower back to provide wearing comfort and avoid disturbance of participants. The most accurate fall risk classification model included data from sit-to-walk and walk-to-sit transitions collected over three days of daily life (mean accuracy = 88.0%). Nine out of 28 included studies revealed information about sensor use in older adults with possible cognitive impairment, but classification models performed slightly worse than those for older adults without cognitive impairment (mean accuracy = 79.0%). Conclusion Fall risk assessment using wearable sensors is feasible in older adults regardless of their cognitive status. Accuracy may vary depending on sensor location, sensor attachment and type of assessment chosen for the recording of sensor data. More research on the use of sensors for objective fall risk assessment in older adults is needed, particularly in older adults with cognitive impairment. Trial registration This systematic review is registered in PROSPERO (CRD42020171118).

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Section: Use Of Sensors To Assess Fall Riskmentioning

confidence: 99%

Sensor-based fall risk assessment in older adults with or without cognitive impairment: a systematic review

Bezold

Krell‐Roesch

Eckert

et al. 2021

Eur Rev Aging Phys Act

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“…The participants (≥60 years old) were recruited from the community [ 60 - 62 , 64 , 65 , 67 - 70 , 72 - 74 , 76 , 77 ], gerontology services [ 71 , 75 , 78 ], both [ 59 , 79 ], or at a hospital [ 66 ]. One study included participants who were referred for geriatric inpatient rehabilitation [ 63 ].…”

Section: Resultsmentioning

confidence: 99%

“…Most of the studies did not describe the characteristics of health conditions of the sample [ 59 , 62 , 64 - 66 , 68 - 72 , 74 - 77 , 79 ]. Only some authors provided quantitative data about the participants’ medication use [ 60 , 61 , 73 , 78 ] and their comorbidities [ 60 , 61 , 63 , 67 , 73 , 78 ].…”

Section: Resultsmentioning

confidence: 99%

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eHealth for Addressing Balance Disorders in the Elderly: Systematic Review

Gašpar¹,

Lapão²

2021

J Med Internet Res

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Background The population is aging on a global scale, triggering vulnerability for chronic multimorbidity, balance disorders, and falls. Falls with injuries are the main cause of accidental death in the elderly population, representing a relevant public health problem. Balance disorder is a major risk factor for falling and represents one of the most frequent reasons for health care demand. The use of information and communication technologies to support distance healthcare (eHealth) represents an opportunity to improve the access and quality of health care services for the elderly. In recent years, several studies have addressed the potential of eHealth devices to assess the balance and risk of falling of elderly people. Remote rehabilitation has also been explored. However, the clinical applicability of these digital solutions for elderly people with balance disorders remains to be studied. Objective The aim of this review was to guide the clinical applicability of eHealth devices in providing the screening, assessment, and treatment of elderly people with balance disorders, but without neurological disease. Methods A systematic review was performed in accordance with the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analysis) statement. Data were obtained through searching the PubMed, Google Scholar, Embase, and SciELO databases. Only randomized controlled trials (RCTs) or quasiexperimental studies (QESs) published between January 2015 and December 2019 were included. The quality of the evidence to respond to the research question was assessed using Joanna Briggs Institute (JBI) Critical Appraisal for RCTs and the JBI Critical Appraisal Checklist for QESs. RCTs were assessed using the Cochrane risk of bias tool. We provide a narrative synthesis of the main outcomes from the included studies. Results Among 1030 unduplicated articles retrieved, 21 articles were included in this review. Twelve studies explored different technology devices to obtain data about balance and risk of falling. Nine studies focused on different types of balance exercise training. A wide range of clinical tests, functional scales, classifications of faller participants, sensor-based tasks, intervention protocols, and follow-up times were used. Only one study described the clinical conditions of the participants. Instrumental tests of the inner ear were neither used as the gold-standard test nor performed in pre and postrehabilitation assessments. Conclusions eHealth has potential for providing additional health care to elderly people with balance disorder and risk of falling. In the included literature, the heterogeneity of populations under study, methodologies, eHealth devices, and time of follow-up did not allow for clear comparison to guide proper clinical applicability. This suggests that more rigorous studies are needed.

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“…By analyzing biomechanical features derived from gait, such as speed, cadence, step length, stance time, or swing time, it is possible to infer whether the observed gait is impaired, and in some cases even distinguish between different disorders that cause gait impairments, and their severity [ 3 ]. The same set of biomechanical gait features can also be used to predict fall risks in elderly populations [ 4 ] or head impacts in athletes [ 5 ].…”

Section: Introductionmentioning

confidence: 99%

Automatic Classification of Gait Impairments Using a Markerless 2D Video-Based System

Verlekar

Soares

Correia

2018

Sensors

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Systemic disorders affecting an individual can cause gait impairments. Successful acquisition and evaluation of features representing such impairments make it possible to estimate the severity of those disorders, which is important information for monitoring patients’ health evolution. However, current state-of-the-art systems perform the acquisition and evaluation of these features in specially equipped laboratories, typically limiting the periodicity of evaluations. With the objective of making health monitoring easier and more accessible, this paper presents a system that performs automatic detection and classification of gait impairments, based on the acquisition and evaluation of biomechanical gait features using a single 2D video camera. The system relies on two different types of features to perform classification: (i) feet-related features, such as step length, step length symmetry, fraction of foot flat during stance phase, normalized step count, speed; and (ii) body-related features, such as the amount of movement while walking, center of gravity shifts and torso orientation. The proposed system uses a support vector machine to decide whether the observed gait is normal or if it belongs to one of three different impaired gait groups. Results show that the proposed system outperforms existing markerless 2D video-based systems, with a classification accuracy of 98.8%.

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

Cited by 31 publications

References 37 publications

Sensor-based fall risk assessment in older adults with or without cognitive impairment: a systematic review

Sensor-based fall risk assessment in older adults with or without cognitive impairment: a systematic review

eHealth for Addressing Balance Disorders in the Elderly: Systematic Review

Automatic Classification of Gait Impairments Using a Markerless 2D Video-Based System

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