Detecting freezing-of-gait during unscripted and unconstrained activity

Cole, Bryan T.; Roy, Serge H.; Nawab, S. Hamid

doi:10.1109/iembs.2011.6091367

Cited by 72 publications

(71 citation statements)

References 7 publications

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“…The results previously obtained by Niazmand et al [23], Cole et al [7] and Bächlin et al [5] were consistently outperformed by the presented approach in its third variation, which has yielded to an average sensitivity and specificity above 94%. Niazmand et al [23] achieved a sensitivity of 88.3% and a specificity of 85.3%.…”

Section: Discussionsupporting

confidence: 60%

“…However, Cole et al [7] achieved a similar level of specificity (97.3%), but with lower sensitivity (82.9%).…”

Section: Discussionmentioning

confidence: 99%

“…Moreover, we only use a single tri-axial accelerometer at the waist (e.g. Cole et al [7]: three tri-axial accelerometers and surface EMG, Niazmand et al [23]: five accelerometers, Bächlin et al [5]: three accelerometers and three gyroscopes). The presented approach offers configuration capabilities since the algorithm can be tuned toward high sensitivity and high specificity by adjusting the thresholds.…”

Section: Discussionmentioning

confidence: 99%

“…festination, akinesia, shuffling and small steps) walking patterns of PD patients based on a NN (using multiple inertial measurement units). The approach by Cole et al [7] yielded to 82.9% sensitivity and 97.3% specificity in detecting FOG (using acceleration and electromyograph (EMG) sensors) with a multi-staged algorithm that utilized a simple linear classifier and a dynamic neural network (DNN). Niazmand et al [23] used an accelerometer based smart garment [22] to extract gait-related features.…”

Section: Introductionmentioning

confidence: 99%

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Detecting freezing of gait with a tri-axial accelerometer in Parkinson’s disease patients

Ahlrichs¹,

Samà²,

Lawo

et al. 2015

Med Biol Eng Comput

105

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Freezing of gait (FOG) is a common motor symptom of Parkinson's Disease (PD), which presents itself as an inability to initiate or continue gait. This paper presents a method to monitor FOG episodes based only on acceleration measurements obtained from a waist-worn device. Three approximations of this method are tested. Initially, FOG is directly detected by a support vector machine (SVM). Then, classifier's outputs are aggregated over time to determine a confidence value, which is used for the final classification of freezing (i.e. second and third approach). All variations are trained with signals of 15 patients and evaluated with signals from another 5 patients. Using a linear SVM kernel, the third approach outperforms results in the literature with 98.7% accuracy and a geometric mean of 96.1%. Moreover, it is investigated whether frequency features are enough to reliably detect FOG. Results show that these features allow the method to detect FOG with accuracies above 90% and that frequency features enable a reliable monitoring of FOG by using simply a waist sensor.

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Section: Discussionsupporting

confidence: 60%

“…However, Cole et al [7] achieved a similar level of specificity (97.3%), but with lower sensitivity (82.9%).…”

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Detecting freezing of gait with a tri-axial accelerometer in Parkinson’s disease patients

Ahlrichs¹,

Samà²,

Lawo

et al. 2015

Med Biol Eng Comput

105

View full text Add to dashboard Cite

show abstract

“…Table I contains an overview of the characteristics and evaluation settings of such systems. Most wearable systems involve accelerometers and/or gyroscopes mounted on the body [Jovanov et al 2009;Djurić-Jovičić et al 2010;Bächlin et al 2010;Mazilu et al 2012;Tripoliti et al 2013] or integrated in garments [Niazmand et al 2011], extended with electroencephalography (EEG) [Handojoseno et al 2012] or electromyography (EMG) [Cole et al 2011].…”

Section: Related Workmentioning

confidence: 99%

A Wearable Assistant for Gait Training for Parkinson’s Disease with Freezing of Gait in Out-of-the-Lab Environments

Mazilu

Blanke

Dorfman

et al. 2015

ACM Trans. Interact. Intell. Syst.

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People with Parkinson's disease (PD) suffer from declining mobility capabilities, which cause a prevalent risk of falling. Commonly, short periods of motor blocks occur during walking, known as freezing of gait (FoG). To slow the progressive decline of motor abilities, people with PD usually undertake stationary motor-training exercises in the clinics or supervised by physiotherapists. We present a wearable system for the support of people with PD and FoG. The system is designed for independent use. It enables motor training and gait assistance at home and other unsupervised environments. The system consists of three components. First, FoG episodes are detected in real time using wearable inertial sensors and a smartphone as the processing unit. Second, a feedback mechanism triggers a rhythmic auditory signal to the user to alleviate freeze episodes in an assistive mode. Third, the smartphone-based application features support for training exercises. Moreover, the system allows unobtrusive and long-term monitoring of the user's clinical condition by transmitting sensing data and statistics to a telemedicine service.We investigate the at-home acceptance of the wearable system in a study with nine PD subjects. Participants deployed and used the system on their own, without any clinical support, at their homes during three protocol sessions in 1 week. Users' feedback suggests an overall positive attitude toward adopting and using the system in their daily life, indicating that the system supports them in improving their gait. Further, in a data-driven analysis with sensing data from five participants, we study whether there is an observable effect on the gait during use of the system. In three out of five subjects, we observed a decrease in FoG duration distributions over the protocol days during gait-training exercises. Moreover, sensing data-driven analysis shows a decrease in FoG duration and FoG number in four out of five participants when they use the system as a gait-assistive tool during normal daily life activities at home. . 2015. A wearable assistant for gait training for Parkinson's disease with freezing of gait in out-of-the-lab environments.

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High‐resolution tracking of motor disorders in Parkinson's disease during unconstrained activity

et al. 2013

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Parkinson’s disease (PD) can present with a variety of motor disorders that fluctuate throughout the day, making assessment a challenging task. Paper-based measurement tools can be burdensome to the patient and clinician and lack the temporal resolution needed to accurately and objectively track changes in motor symptom severity throughout the day. Wearable sensor-based systems that continuously monitor PD motor disorders may help to solve this problem, although critical shortcomings persist in identifying multiple disorders at high temporal resolution during unconstrained activity. The purpose of this study was to advance the current state of the art by (1) introducing hybrid sensor technology to concurrently acquire surface electromyographic (sEMG) and accelerometer data during unconstrained activity and (2) analyzing the data using dynamic neural network algorithms to capture the evolving temporal characteristics of the sensor data and improve motor disorder recognition of tremor and dyskinesia. Algorithms were trained (n = 11 patients) and tested (n = 8 patients; n = 4 controls) to recognize tremor and dyskinesia at 1-second resolution based on sensor data features and expert annotation of video recording during 4-hour monitoring periods of unconstrained daily activity. The algorithms were able to make accurate distinctions between tremor, dyskinesia, and normal movement despite the presence of diverse voluntary activity. Motor disorder severity classifications averaged 94.9% sensitivity and 97.1% specificity based on 1 sensor per symptomatic limb. These initial findings indicate that new sensor technology and software algorithms can be effective in enhancing wearable sensor-based system performance for monitoring PD motor disorders during unconstrained activities.

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Detecting freezing-of-gait during unscripted and unconstrained activity

Cited by 72 publications

References 7 publications

Detecting freezing of gait with a tri-axial accelerometer in Parkinson’s disease patients

Detecting freezing of gait with a tri-axial accelerometer in Parkinson’s disease patients

A Wearable Assistant for Gait Training for Parkinson’s Disease with Freezing of Gait in Out-of-the-Lab Environments

High‐resolution tracking of motor disorders in Parkinson's disease during unconstrained activity

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