Wireless devices to restart walking during an episode of FOG on patients with Parkinson's disease

Punin, C.; Barzallo, Boris; Huerta, Mónica; Bermeo, A.; Bravo, M.; Llumiguano, C.

doi:10.1109/etcm.2017.8247520

Cited by 15 publications

(34 citation statements)

References 30 publications

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“…FOG detection methods vary in complexity, with the simplest models directly comparing wearable sensor variables to thresholds [29,41,44,46,53,63,73,123]. Threshold methods tended to have poorer detection performance but faster processing time, making them potentially useful in real-time systems [24,70,77,79,92,93]. To improve classification performance, features that can better differentiate between FOG and typical PD gait have been used, such as Fourier transforms [29,34,35,41,44,53,65,69,78], wavelet transforms [51,56,63,71,79,83,91,92,93,96], k-index [59,60,61,62,72,73], freezing of gait criterion (FOGC) [46], freezing of gait detection on glasses (FOGDOG) [70], R-index [94], and the widely-used freeze index [29].…”

Section: Discussionmentioning

confidence: 99%

“…Frequency domain features include freeze index (FI) [29], which was the most widely-used frequency domain feature [23,24,25,29,31,32,33,34,42,44,49,53,54,64,65,69,71,77,78,80,86,88,89,98], peak amplitude and corresponding frequency [40,41,64], standard deviation in frequency domain [50,57,64,74], spectral density centre of mass [50,57,66,74,80,81,86,96], and power of the signal in specific frequency bands [24,25,31,32,33,34,40,42,52,54,58,75,80]. While Fourier transforms are typically used to convert signals from the time domain to frequency domain, Fourier transform limitations have led to increased usage of wavelet approaches [51,56,63,71,79,83,91,92,93,96].…”

Section: Discussionmentioning

confidence: 99%

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Wearable-Sensor-Based Detection and Prediction of Freezing of Gait in Parkinson’s Disease: A Review

Pardoel

Kofman

Nantel

et al. 2019

Sensors

137

111

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Freezing of gait (FOG) is a serious gait disturbance, common in mid- and late-stage Parkinson’s disease, that affects mobility and increases fall risk. Wearable sensors have been used to detect and predict FOG with the ultimate aim of preventing freezes or reducing their effect using gait monitoring and assistive devices. This review presents and assesses the state of the art of FOG detection and prediction using wearable sensors, with the intention of providing guidance on current knowledge, and identifying knowledge gaps that need to be filled and challenges to be considered in future studies. This review searched the Scopus, PubMed, and Web of Science databases to identify studies that used wearable sensors to detect or predict FOG episodes in Parkinson’s disease. Following screening, 74 publications were included, comprising 68 publications detecting FOG, seven predicting FOG, and one in both categories. Details were extracted regarding participants, walking task, sensor type and body location, detection or prediction approach, feature extraction and selection, classification method, and detection and prediction performance. The results showed that increasingly complex machine-learning algorithms combined with diverse feature sets improved FOG detection. The lack of large FOG datasets and highly person-specific FOG manifestation were common challenges. Transfer learning and semi-supervised learning were promising for FOG detection and prediction since they provided person-specific tuning while preserving model generalization.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Wearable-Sensor-Based Detection and Prediction of Freezing of Gait in Parkinson’s Disease: A Review

Pardoel

Kofman

Nantel

et al. 2019

Sensors

137

111

View full text Add to dashboard Cite

show abstract

“…The wavelets operate analogously to Fourier analysis in some applications. The main difference that wavelets have with Fourier transforms is that wavelets perform local analysis, which makes them appropriate for the analysis of signals in the time-frequency domain, while Fourier transforms are global [43,44]. Wavelet techniques allow to divide a complex function into simpler ones and study them separately.…”

Section: Mathematical Tool For Processingmentioning

confidence: 99%

A Non-Invasive Medical Device for Parkinson’s Patients with Episodes of Freezing of Gait

Punin

Barzallo

Clotet

et al. 2019

Sensors

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A critical symptom of Parkinson’s disease (PD) is the occurrence of Freezing of Gait (FOG), an episodic disorder that causes frequent falls and consequential injuries in PD patients. There are various auditory, visual, tactile, and other types of stimulation interventions that can be used to induce PD patients to escape FOG episodes. In this article, we describe a low cost wearable system for non-invasive gait monitoring and external delivery of superficial vibratory stimulation to the lower extremities triggered by FOG episodes. The intended purpose is to reduce the duration of the FOG episode, thus allowing prompt resumption of gait to prevent major injuries. The system, based on an Android mobile application, uses a tri-axial accelerometer device for gait data acquisition. Gathered data is processed via a discrete wavelet transform-based algorithm that precisely detects FOG episodes in real time. Detection activates external vibratory stimulation of the legs to reduce FOG time. The integration of detection and stimulation in one low cost device is the chief novel contribution of this work. We present analyses of sensitivity, specificity and effectiveness of the proposed system to validate its usefulness.

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“…All systems used vibratory motors based on rotary and linear electromagnetic actuators. The eccentric rotating mass (ERM) motors identified in [19,[22][23][24][25][26][27]29] correspond to a rotary electromagnetic actuator and the linear resonance actuator (LRA) motors identified in [20,21,24] concern to linear electromagnetic actuators. The number of actuators used was usually one [20,21,27] and two [19,22,23,28], but when the actuators were placed around the torso, hip, or head, the used number increased to four [24,29] and eight [25,26].…”

Section: Technology Supporting Vbsmentioning

confidence: 99%

Vibrotactile biofeedback devices in Parkinson’s disease: a narrative review

Gonçalves

Rodrigues

Santos

2021

Med Biol Eng Comput

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Parkinson's disease (PD) is often associated with a vast list of gait-associated disabilities, for which there is still a limited pharmacological/surgical treatment efficacy. Therefore, alternative approaches have emerged as vibrotactile biofeedback systems (VBS). This review aims to focus on the technologies supporting VBS and identify their effects on improving gait-associated disabilities by verifying how VBS were applied and validated with end-users. It is expected to furnish guidance to researchers looking to enhance the effectiveness of future vibrotactile cueing systems. The use of vibrotactile cues has proved to be relevant and attractive, as positive results have been obtained in patients' gait performance, suitability in any environment, and easy adherence. There seems to be a preference in developing VBS to mitigate freezing of gait, to improve balance, to overcome the risk of fall, and a prevalent use to apply miniaturized wearable actuators and sensors. Most studies implemented a biofeedback loop able to provide rescue strategies during or after the detection of a gait-associated disability. However, there is a need of more clinical evidence and inclusion of experimental sessions to evaluate if the biofeedback was effectively integrated into the patients' motor system.

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Wireless devices to restart walking during an episode of FOG on patients with Parkinson's disease

Cited by 15 publications

References 30 publications

Wearable-Sensor-Based Detection and Prediction of Freezing of Gait in Parkinson’s Disease: A Review

Wearable-Sensor-Based Detection and Prediction of Freezing of Gait in Parkinson’s Disease: A Review

A Non-Invasive Medical Device for Parkinson’s Patients with Episodes of Freezing of Gait

Vibrotactile biofeedback devices in Parkinson’s disease: a narrative review

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