Measurement of Accelerometry-based Gait Parameters in People with and without Dementia in the Field

Gietzelt, Matthias; Wolf, Klaus-Hendrik; Kohlmann, Martin; Marschollek, Michael; Haux, Reinhold

doi:10.3414/me12-02-0009

Cited by 33 publications

(9 citation statements)

References 32 publications

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“…Placement of sensors on the affected leg alone keeps the system easy to use and allows us to overcome problems with the limited Bluetooth ® transmission protocol. Although the parameters of step count [ 20 ] and step symmetry [ 21 ] can be obtained from the pelvic sensor, no accurate information can be obtained about the single-leg stance of the non-operated side or the double-leg stance. These parameters are often referred to in the literature; however, we do believe that our outputted gait symmetry is an equivalent model with which to evaluate limping.…”

Section: Resultsmentioning

confidence: 99%

“…Our validated proprietary software, which was developed in the platform-independent programming language R for statistical computing and graphics, was used to determine general parameters of gait (e.g., walking speed, running speed, step cadence, step length and step symmetry). A detailed description of self-developed algorithms from our institute can be found in [ 20 , 21 ]. To achieve the maximum possible precision, we computed speed, cadence, and step length based on a predetermined path length that was used in accordance with the study protocol and annotated while recording.…”

Section: Experimental Methodologymentioning

confidence: 99%

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Clinical Evaluation of a Mobile Sensor-Based Gait Analysis Method for Outcome Measurement after Knee Arthroplasty

Calließ

Bocklage

Karkosch

et al. 2014

Sensors

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Clinical scores and motion-capturing gait analysis are today's gold standard for outcome measurement after knee arthroplasty, although they are criticized for bias and their ability to reflect patients' actual quality of life has been questioned. In this context, mobile gait analysis systems have been introduced to overcome some of these limitations. This study used a previously developed mobile gait analysis system comprising three inertial sensor units to evaluate daily activities and sports. The sensors were taped to the lumbosacral junction and the thigh and shank of the affected limb. The annotated raw data was evaluated using our validated proprietary software. Six patients undergoing knee arthroplasty were examined the day before and 12 months after surgery. All patients reported a satisfactory outcome, although four patients still had limitations in their desired activities. In this context, feasible running speed demonstrated a good correlation with reported impairments in sports-related activities. Notably, knee flexion angle while descending stairs and the ability to stop abruptly when running exhibited good correlation with the clinical stability and proprioception of the knee. Moreover, fatigue effects were displayed in some patients. The introduced system appears to be suitable for outcome measurement after knee arthroplasty and has the potential to overcome some of the limitations of stationary gait labs while gathering additional meaningful parameters regarding the force limits of the knee.

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Section: Resultsmentioning

confidence: 99%

Section: Experimental Methodologymentioning

confidence: 99%

Clinical Evaluation of a Mobile Sensor-Based Gait Analysis Method for Outcome Measurement after Knee Arthroplasty

Calließ

Bocklage

Karkosch

et al. 2014

Sensors

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“…Their work provides some of the inspiration for our research. Gietzelt et al [16] have also used accelerometers as a tool for measurement of gait parameters in people with dementia, achieving good accuracy in the most relevant parameters.…”

Section: Resultsmentioning

confidence: 99%

Classification of Exacerbation Episodes in Chronic Obstructive Pulmonary Disease Patients

Dias

Gorzelniak²,

Schultz³

et al. 2014

Methods Inf Med

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Summary Background: Chronic obstructive pulmonary disease (COPD) is a progressive disease affecting the airways, which constitutes a major cause of chronic morbidity and a significant economic and social burden throughout the world. Despite the fact that in COPD patients exacerbations are common acute events causing significant and often fatal worsening of symptoms, an accurate prognostication continues to be difficult. Objectives: To build computational models capable of distinguishing between normal life days from exacerbation days in COPD patients, based on physical activity measured by accelerometers. Methods: We recruited 58 patients suffering from COPD and measured their physical activity with accelerometers for 10 days or more, from August 2009 to March 2010. During this period we recorded six exacerbation episodes in the patients, accounting for 37 days. We were able to analyse data for 52 patients (369 patient days), and extracted three distinct sets of features from the data, one set of basic features such as average, one set based on the frequency domain and the last exploring the cross-information among sensors pairs. These were used by three machine-learning techniques (logarith mic regression, neural networks, support vector machines) to distinguish days with exacerbation events from normal days. Results: The support vector machine clas -sifier achieved an AUC of 90% ± 9, when supplied with a set of features resulting from sequential feature selection method. Neural networks achieved an AUC of 83% ± 16 and the logarithmic regression an AUC of 67% ± 15. Conclusions: None of the individual feature sets provided robust for reasonable classi -fication of PA recording days. Our results indicate that this approach has the potential to extract useful information for, but are not robust enough for medical application of the system.

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“…A key advantage in contrast to conventional approaches is that portable devices can provide a source of ecological information and, therefore, certain kinds of data that cannot be obtained in the clinical setting or supervised laboratory environments. For instance, different works report the methodology for detecting gait episodes and estimating gait parameters from accelerometric signals [13,14], which have been demonstrated to be able to distinguish both patients with dementia [14] and even MCI [15] from healthy controls. Additionally, there is increasing evidence that some gait abnormalities are present since very early stages of dementia, even when they are still unapparent in clinical assessment.…”

Section: Related Workmentioning

confidence: 99%

A Convolutional Neural Network-Based Method for Human Movement Patterns Classification in Alzheimer’s Disease

Bringas

Salomón²,

Duque

et al. 2019

13th International Conference on Ubiquitous Computing and Ambient ‪Intelligence UCAmI 2019‬

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Alzheimer's disease (AD) constitutes a neurodegenerative pathology that presents mobility disorders as one of its earliest symptoms. Current smartphones integrate accelerometers that can be used to collect mobility data of Alzheimer's patients. This paper describes a method that processes these accelerometer data and a convolutional neural network (CNN) that classifies the stage of the disease according to the mobility patterns of the patient. The method is applied in a case study with 35 Alzheimer's patients, in which a classification success rate of 91% was obtained.

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Measurement of Accelerometry-based Gait Parameters in People with and without Dementia in the Field

Abstract: The results showed a much higher amount of walking episodes in field studies compared to supervised clinical trials. The classification showed a high accuracy in distinguishing between both groups.

Cited by 33 publications

References 32 publications

Clinical Evaluation of a Mobile Sensor-Based Gait Analysis Method for Outcome Measurement after Knee Arthroplasty

Clinical Evaluation of a Mobile Sensor-Based Gait Analysis Method for Outcome Measurement after Knee Arthroplasty

Classification of Exacerbation Episodes in Chronic Obstructive Pulmonary Disease Patients

A Convolutional Neural Network-Based Method for Human Movement Patterns Classification in Alzheimer’s Disease

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