Automatic Identification and Classification of Freezing of Gait Episodes in Parkinson's Disease Patients

Djurić-Jovičić, Milica; Jovičić, Nenad S.; Radovanović, Saša; Stanković, Iva; Popović, Mirjana; Kostić, Vladimir

doi:10.1109/tnsre.2013.2287241

Cited by 63 publications

(43 citation statements)

References 38 publications

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“…Among the 23 articles investigating FOG detection, 18 reported measures of validation performance (e.g., sensitivity, specificity, or accuracy) [17, 36–45, 47–49, 52–55], three studies used correlation measures, correlating the wearable-derived measure with the period of freezing or number of FOG events [50, 51, 56], and two studies did not report validity measures [28, 46]. …”

Section: Resultsmentioning

confidence: 99%

“…Omitting small FOG episodes may lead to inaccurate estimates of FOG detection rates. A comprehensive definition such as that used by Djuric–Jovici and colleagues [47], differentiating between FOG with trembling and FOG with complete motor blocks prior to video labeling and test properties, seems to address the problem by incorporating different types of FOG events. However, this definition was not used in other studies.…”

Section: Discussionmentioning

confidence: 99%

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Freezing of gait and fall detection in Parkinson’s disease using wearable sensors: a systematic review

et al. 2017

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Despite the large number of studies that have investigated the use of wearable sensors to detect gait disturbances such as Freezing of gait (FOG) and falls, there is little consensus regarding appropriate methodologies for how to optimally apply such devices. Here, an overview of the use of wearable systems to assess FOG and falls in Parkinson’s disease (PD) and validation performance is presented. A systematic search in the PubMed and Web of Science databases was performed using a group of concept key words. The final search was performed in January 2017, and articles were selected based upon a set of eligibility criteria. In total, 27 articles were selected. Of those, 23 related to FOG and 4 to falls. FOG studies were performed in either laboratory or home settings, with sample sizes ranging from 1 PD up to 48 PD presenting Hoehn and Yahr stage from 2 to 4. The shin was the most common sensor location and accelerometer was the most frequently used sensor type. Validity measures ranged from 73–100% for sensitivity and 67–100% for specificity. Falls and fall risk studies were all home-based, including samples sizes of 1 PD up to 107 PD, mostly using one sensor containing accelerometers, worn at various body locations. Despite the promising validation initiatives reported in these studies, they were all performed in relatively small sample sizes, and there was a significant variability in outcomes measured and results reported. Given these limitations, the validation of sensor-derived assessments of PD features would benefit from more focused research efforts, increased collaboration among researchers, aligning data collection protocols, and sharing data sets.

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Section: Resultsmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Freezing of gait and fall detection in Parkinson’s disease using wearable sensors: a systematic review

et al. 2017

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“…Zijlstra [22] showed that patients with PD displayed a lower angular velocity during the extension phase of standing up using an inertial sensor on the hip. Similarly, parameters such as trunk angle [23], freezing of gait, [24–26]and gait parameters [27–29] (stride time, cadence, range of motion, etc.) have been shown to change significantly in people with PD when compared to healthy older adults during common daily living activities.…”

Section: Introductionmentioning

confidence: 99%

Auto detection and segmentation of daily living activities during a Timed Up and Go task in people with Parkinson’s disease using multiple inertial sensors

Nguyen

Lebel

Boissy

et al. 2017

J NeuroEngineering Rehabil

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BackgroundWearable sensors have the potential to provide clinicians with access to motor performance of people with movement disorder as they undergo intervention. However, sensor data often have to be manually classified and segmented before they can be processed into clinical metrics. This process can be time consuming. We recently proposed detection and segmentation algorithms based on peak detection using Inertial Measurement Units (IMUs) to automatically identify and isolate common activities during daily living such as standing up, walking, turning, and sitting down. These algorithms were developed using a homogenous population of healthy older adults. The aim of this study was to investigate the transferability of these algorithms in people with Parkinson’s disease (PD).MethodsA modified Timed Up And Go task was used since it is comprised of these activities, all performed in a continuous fashion. Twelve older adults diagnosed with early PD (Hoehn & Yahr ≤ 2) were recruited for the study and performed three trials of a 10 and 5-m TUG during OFF state. They were outfitted with 17 IMUs covering each body segment. Raw data from IMUs were detrended, normalized and filtered to reveal kinematics peaks that corresponded to different activities. Segmentation was accomplished by identifying the first minimum or maximum to the right and the left of these peaks. Segmentation times were compared to results from two examiners who visually segmented the activities. Specificity and sensitivity were used to evaluate the accuracy of the detection algorithms.ResultsUsing the same IMUs and algorithms developed in the previous study, we were able to detect these activities with 97.6% sensitivity and 92.7% specificity (n = 432) in PD population. However, with modifications to the IMUs selection, we were able to detect these activities with 100% accuracy. Similarly, applying the same segmentation to PD population, we were able to isolate these activities within ~500 ms of the visual segmentation. Re-optimizing the filtering frequencies, we were able to reduce this difference to ~400 ms.ConclusionsThis study demonstrates the agility and transferability of using a system of IMUs to accurately detect and segment activities in daily living in people with movement disorders.

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“…Applied to neurological diseases, it represents an extremely helpful marker for practitioners to analyze pathological effects on gait and therapy efficiency. Preliminary work showed that monitoring the instantaneous stride length gave a crucial information to online detect or anticipate freezing of gait (FOG, an episodic inability to generate effective stepping) in patients suffering from Parkinson's disease [4] [5]. Measuring the physical activity of those patients and quantifying FOG events could also enable to differentiate "OFF" times, a state of decreased mobility associated to a low L-dopa level, and "ON" times, or periods when the medication is working and symptoms are controlled [6].…”

Section: Introductionmentioning

confidence: 99%

Implementation and Validation of a Stride Length Estimation Algorithm, Using a Single Basic Inertial Sensor on Healthy Subjects and Patients Suffering from Parkinson’s Disease

Sijobert¹,

Benoussaad²,

Denys³

et al. 2015

Health

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As low cost and highly portable sensors, inertial measurements units (IMU) have become increasingly used in gait analysis, embodying an efficient alternative to motion capture systems. Meanwhile, being able to compute reliably accurate spatial gait parameters using few sensors remains a relatively complex problematic. Providing a clinical oriented solution, our study presents a gyrometer and accelerometer based algorithm for stride length estimation. Compared to most of the numerous existing works where only an averaged stride length is computed from several IMU, or where the use of the magnetometer is incompatible with everyday use, our challenge here has been to extract each individual stride length in an easy-to-use algorithm requiring only one inertial sensor attached to the subject shank. Our results were validated on healthy subjects and patients suffering from Parkinson's disease (PD). Estimated stride lengths were compared to GAITRite © walkway system data: the mean error over all the strides was less than 6% for healthy group and 10.3% for PD group. This method provides a reliable portable solution for monitoring the instantaneous stride length and opens the way to promising applications.

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Automatic Identification and Classification of Freezing of Gait Episodes in Parkinson's Disease Patients

Cited by 63 publications

References 38 publications

Freezing of gait and fall detection in Parkinson’s disease using wearable sensors: a systematic review

Freezing of gait and fall detection in Parkinson’s disease using wearable sensors: a systematic review

Auto detection and segmentation of daily living activities during a Timed Up and Go task in people with Parkinson’s disease using multiple inertial sensors

Implementation and Validation of a Stride Length Estimation Algorithm, Using a Single Basic Inertial Sensor on Healthy Subjects and Patients Suffering from Parkinson’s Disease

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