Subsequence dynamic time warping as a method for robust step segmentation using gyroscope signals of daily life activities

Barth, Jens; Oberndorfer, Cäcilia; Kugler, Patrick; Schuldhaus, Dominik; Winkler, Jürgen; Klucken, Jochen; Eskofier, Bjoern M.

doi:10.1109/embc.2013.6611104

Cited by 35 publications

(44 citation statements)

References 9 publications

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“…Stride segmentation was achieved using a previously published method [31]. There were some erroneously detected strides that had to be corrected by hand.…”

Section: Discussionmentioning

confidence: 99%

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Inertial Sensor-Based Stride Parameter Calculation From Gait Sequences in Geriatric Patients

Rampp¹,

Barth²,

Schülein

et al. 2015

IEEE Trans. Biomed. Eng.

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256

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A detailed and quantitative gait analysis can provide evidence of various gait impairments in elderly people. To provide an objective decision-making basis for gait analysis, simple applicable tests analyzing a high number of strides are required. A mobile gait analysis system, which is mounted on shoes, can fulfill these requirements. This paper presents a method for computing clinically relevant temporal and spatial gait parameters. Therefore, an accelerometer and a gyroscope were positioned laterally below each ankle joint. Temporal gait events were detected by searching for characteristic features in the signals. To calculate stride length, the gravity compensated accelerometer signal was double integrated, and sensor drift was modeled using a piece-wise defined linear function. The presented method was validated using GAITRite-based gait parameters from 101 patients (average age 82.1 years). Subjects performed a normal walking test with and without a wheeled walker. The parameters stride length and stride time showed a correlation of 0.93 and 0.95 between both systems. The absolute error of stride length was 6.26 cm on normal walking test. The developed system as well as the GAITRite showed an increased stride length, when using a four-wheeled walker as walking aid. However, the walking aid interfered with the automated analysis of the GAITRite system, but not with the inertial sensor-based approach. In summary, an algorithm for the calculation of clinically relevant gait parameters derived from inertial sensors is applicable in the diagnostic workup and also during long-term monitoring approaches in the elderly population.

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“…Stride segmentation was achieved using a previously published method [31]. There were some erroneously detected strides that had to be corrected by hand.…”

Section: Discussionmentioning

confidence: 99%

“…For stride segmentation, a previously developed algorithm on the basis of subsequent dynamic time warping was used [31]. To apply the stride segmentation, a template of a single stride was defined manually from the gyroscope z-axis.…”

Section: Stride Segmentationmentioning

confidence: 99%

Inertial Sensor-Based Stride Parameter Calculation From Gait Sequences in Geriatric Patients

Rampp¹,

Barth²,

Schülein

et al. 2015

IEEE Trans. Biomed. Eng.

Self Cite

256

275

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show abstract

“…In research, data collected from wearable sensors mounted close to the patient's body are used to quantify patients' gait. This is generally performed in three stages: (1) gait segmentation [52]- [55], (2) gait parameter extraction [56]- [59], and (3) gait parameter analysis [60]- [63]. Gait segmentation is the process by which similar sub-sequences are segmented from the total recording length.…”

Section: Transforming Data Into Knowledgementioning

confidence: 99%

An Emerging Era in the Management of Parkinson's Disease: Wearable Technologies and the Internet of Things

Pasluosta

Gaßner

Winkler

et al. 2015

IEEE J. Biomed. Health Inform.

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286

154

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Current challenges demand a profound restructuration of the global healthcare system. A more efficient system is required to cope with the growing world population and increased life expectancy, which is associated with a marked prevalence of chronic neurological disorders such as Parkinson's disease (PD). One possible approach to meet this demand is a laterally distributed platform such as the Internet of Things (IoT). Real-time motion metrics in PD could be obtained virtually in any scenario by placing lightweight wearable sensors in the patient's clothes and connecting them to a medical database through mobile devices such as cell phones or tablets. Technologies exist to collect huge amounts of patient data not only during regular medical visits but also at home during activities of daily life. These data could be fed into intelligent algorithms to first discriminate relevant threatening conditions, adjust medications based on online obtained physical deficits, and facilitate strategies to modify disease progression. A major impact of this approach lies in its efficiency, by maximizing resources and drastically improving the patient experience. The patient participates actively in disease management via combined objective device- and self-assessment and by sharing information within both medical and peer groups. Here, we review and discuss the existing wearable technologies and the Internet-of-Things concept applied to PD, with an emphasis on how this technological platform may lead to a shift in paradigm in terms of diagnostics and treatment.

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“…More recent work with bilateral lower limb sensors has provided promising results for moderate to severe conditions but is still rare in the literature (12,20). For these reasons, the use of templates or several techniques based on machine learning (18,21) or Dynamic Time Warping (22)(23)(24) has been advocated in several articles (25,26) as a way to automatically learn the characteristics of a cohort. However, heavily altered steps might not be caught by these templates (14,25,27).…”

Section: Introductionmentioning

confidence: 99%

Personalized Template-Based Step Detection From Inertial Measurement Units Signals in Multiple Sclerosis

et al. 2020

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Background: Objective gait assessment is key for the follow-up of patients with progressive multiple sclerosis (pMS). Inertial measurement units (IMUs) provide reliable and yet easy quantitative gait assessment in routine clinical settings. However, to the best of our knowledge, no automated step-detection algorithm performs well in detecting severely altered pMS gait.Method: This article elaborates on a step-detection method based on personalized templates tested against a gold standard. Twenty-two individuals with pMS and 10 young healthy subjects (HSs) were instructed to walk on an electronic walkway wearing synchronized IMUs. Templates were derived from the IMU signals by using Initial and Final Contact times given by the walkway. These were used to detect steps from other gait trials of the same individual (intra-individual template-based detection, IITD) or another participant from the same group (pMS or HS) (intra-group template-based detection, IGTD). All participants were seen twice with a 6-month interval, with two measurements performed at each visit. Performance and accuracy metrics were computed, along with a similarity index (SId), which was computed as the mean distance between detected steps and their respective closest template.Results: For HS participants, both the IITD and the IGTD algorithms had precision and recall of 1.00 for detecting steps. For pMS participants, precision and recall ranged from 0.94 to 1.00 for IITD and 0.85 to 0.95 for IGTD depending on the level of disability. The SId was correlated with performance and the accuracy of the result. An SId threshold of 0.957 (IITD) and 0.963 (IGTD) could rule out decreased performance (F-measure ≤ 0.95), with negative predictive values of 0.99 and 0.96 with the IITD and IGTD algorithms. Also, the SId computed with the IITD and IGTD algorithms could distinguish individuals showing changes at 6-month follow-up.Conclusion: This personalized step-detection method has high performance for detecting steps in pMS individuals with severely altered gait. The algorithm can be Vienne-Jumeau et al. PersonalizedStep Detection in Multiple Sclerosis self-evaluating with the SI, which gives a measure of the confidence the clinician can have in the detection. What is more, the SId can be used as a biomarker of change in disease severity occurring between the two measurement times.

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Subsequence dynamic time warping as a method for robust step segmentation using gyroscope signals of daily life activities

Cited by 35 publications

References 9 publications

Inertial Sensor-Based Stride Parameter Calculation From Gait Sequences in Geriatric Patients

Inertial Sensor-Based Stride Parameter Calculation From Gait Sequences in Geriatric Patients

An Emerging Era in the Management of Parkinson's Disease: Wearable Technologies and the Internet of Things

Personalized Template-Based Step Detection From Inertial Measurement Units Signals in Multiple Sclerosis

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