Gait and Balance Analysis for Patients With Alzheimer's Disease Using an Inertial-Sensor-Based Wearable Instrument

Hsu, Yu Liang; Chung, Pau Choo; Wang, Wei Hsin; Pai, Ming‐Chyi; Wang, Chun Yao; Lin, Chien Wen; Wu, Hao; Wang, Jeen Shing

doi:10.1109/jbhi.2014.2325413

Cited by 113 publications

(69 citation statements)

References 22 publications

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“…Studies included dual-task (Hsu et al, 2014; Howell et al, 2015; Gillain et al, 2016; Henderson et al, 2016; Jaywant et al, 2016), eyes-closed walk, narrow-step width, and obstacle negotiation. Except for 4 studies, all concerned neurological patients.…”

Section: Resultsmentioning

confidence: 99%

Inertial Sensors to Assess Gait Quality in Patients with Neurological Disorders: A Systematic Review of Technical and Analytical Challenges

et al. 2017

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Gait disorders are major causes of falls in patients with neurological diseases. Understanding these disorders allows prevention and better insights into underlying diseases. InertiaLocoGraphy (ILG) –the quantification of gait by using inertial measurement units (IMUs) –shows great potential to address this public health challenge, but protocols vary widely and normative values of gait parameters are still unavailable. This systematic review critically compares ILG protocols, questions features extracted from inertial signals and proposes a semeiological analysis of clinimetric characteristics for use in neurological clinical routine. For this systematic review, PubMed, Cochrane and EMBASE were searched for articles assessing gait quality by using IMUs that were published from January 1, 2014 to August 31, 2016. ILG was used to assess gait in a wide range of neurological disorders – including Parkinson disease, mild cognitive impairment, Alzheimer disease, cerebral palsy, and cerebellar atrophy – as well as in the faller or frail older population and in people presenting rheumatological pathologies. However, results have not yet been driving changes in clinical practice. One reason could be that studies mainly aimed at comparing pathological gait to healthy gait, but there is stronger need for semiological descriptions of gait perturbation, severity or prognostic assessment. Furthermore, protocols used to assess gait using IMUs are too many. Likely, outcomes are highly heterogeneous and difficult to compare across large panels of studies. Therefore, homogenization is needed to foster the use of ILG to assess gait quality in neurological routine practice. The pros and cons of each protocol are emphasized so that a compromise can be reached. As well, analysis of seven complementary clinical criteria (springiness, sturdiness, smoothness, steadiness, stability, symmetry, synchronization) is advocated.

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Section: Resultsmentioning

confidence: 99%

Inertial Sensors to Assess Gait Quality in Patients with Neurological Disorders: A Systematic Review of Technical and Analytical Challenges

et al. 2017

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“…A widely-adopted method for IC/FO detection is peak detection on the raw acceleration or gyroscope (angular velocity) signals. IMUs placed on either the top of the feet [6,15,22,25] or on the ankles/shanks [16,17] are used for this purpose. Peak detection can be further complemented with additional features, such as the derivative of the acceleration signal and fed into a hidden Markov model (HMM) to detect the underlying states of a stride more accurately [18].…”

Section: Related Work On Gait Analysis With Imusmentioning

confidence: 99%

Inertial Sensor-Based Robust Gait Analysis in Non-Hospital Settings for Neurological Disorders

Tunca

Pehlivan

Ak³

et al. 2017

Sensors

126

118

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The gold standards for gait analysis are instrumented walkways and marker-based motion capture systems, which require costly infrastructure and are only available in hospitals and specialized gait clinics. Even though the completeness and the accuracy of these systems are unquestionable, a mobile and pervasive gait analysis alternative suitable for non-hospital settings is a clinical necessity. Using inertial sensors for gait analysis has been well explored in the literature with promising results. However, the majority of the existing work does not consider realistic conditions where data collection and sensor placement imperfections are imminent. Moreover, some of the underlying assumptions of the existing work are not compatible with pathological gait, decreasing the accuracy. To overcome these challenges, we propose a foot-mounted inertial sensor-based gait analysis system that extends the well-established zero-velocity update and Kalman filtering methodology. Our system copes with various cases of data collection difficulties and relaxes some of the assumptions invalid for pathological gait (e.g., the assumption of observing a heel strike during a gait cycle). The system is able to extract a rich set of standard gait metrics, including stride length, cadence, cycle time, stance time, swing time, stance ratio, speed, maximum/minimum clearance and turning rate. We validated the spatio-temporal accuracy of the proposed system by comparing the stride length and swing time output with an IR depth-camera-based reference system on a dataset comprised of 22 subjects. Furthermore, to highlight the clinical applicability of the system, we present a clinical discussion of the extracted metrics on a disjoint dataset of 17 subjects with various neurological conditions.

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“…The heel strike incidences were considered as the starting and ending points of a gait cycle as defined in previous research [1]. Minima are detected to be the segmentation flags, where data fragment between two flags is one complete gait cycle [10]. However when the dataset has large fluctuation or significant outliers, false detection can occur.…”

Section: B) Find a Dataset To Be The Segmentation Referencementioning

confidence: 99%

“…In this test, thesignal vector magnitude of acceleration gait signal with sampling rate of 30 Hz is segmented [10]. The signal vector magnitude is defined in (9).…”

Section: Performance Analysismentioning

confidence: 99%

A robust algorithm for gait cycle segmentation

Jiang

Wang

Kyrarini

et al. 2017

2017 25th European Signal Processing Conference (EUSIPCO)

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Abstract-In this paper, a robust algorithm for gait cycle segmentation is proposed based on a peak detection approach. The proposed algorithm is less influenced by noise and outliers and is capable of segmenting gait cycles from different types of gait signals recorded using different sensor systems. The presented algorithm has enhanced ability to segment gait cycles by eliminating the false peaks and interpolating the missing peaks. The variance of segmented cycles' lengths is computed as a criterion for evaluating the performance of segmentation. The proposed algorithm is tested on gait signals of patients diagnosed with Parkinson's disease collected from three databases. The segmentation results on three types of gait signals demonstrate the capability of the proposed algorithm to segment gait cycles accurately, and have achieved better performance than the original peak detection methods.

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Gait and Balance Analysis for Patients With Alzheimer's Disease Using an Inertial-Sensor-Based Wearable Instrument

Cited by 113 publications

References 22 publications

Inertial Sensors to Assess Gait Quality in Patients with Neurological Disorders: A Systematic Review of Technical and Analytical Challenges

Inertial Sensors to Assess Gait Quality in Patients with Neurological Disorders: A Systematic Review of Technical and Analytical Challenges

Inertial Sensor-Based Robust Gait Analysis in Non-Hospital Settings for Neurological Disorders

A robust algorithm for gait cycle segmentation

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