Validation of Freezing-of-Gait Monitoring Using Smartphone

Kim, Han Byul; Lee, Hong Ji; Lee, Woong-Woo; Kim, Sang Kyong; Jeon, Hyo Seon; Park, Hye Young; Shin, Chaewon; Yi, Won-Jin; Jeon, Beomseok; Park, Kwang S.

doi:10.1089/tmj.2017.0215

Cited by 36 publications

(48 citation statements)

References 25 publications

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“…Indeed, smartphones are currently equipped with accelerometers and gyroscopes, with internet connectivity, and they will not require the patient to wear another device. So far, this avenue is more theoretical, given that no studies have been carried out in the home, but some evidence exists demonstrating its feasibility in a laboratory setting . New challenges arise here, for example the obvious question where the smartphone should be carried in order to perform optimally.…”

Section: Assessing the Presence And Severity Of Fog With Wearable Senmentioning

confidence: 99%

Clinical and methodological challenges for assessing freezing of gait: Future perspectives

Bloem²,

et al. 2019

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A BS TRACT: Freezing of gait, defined as sudden and usually brief episodes of inability to produce effective stepping, often results in falls and is both disabling and common in parkinsonism. In this narrative review, sprung from the 2nd International Workshop on freezing of gait in Leuven, we summarize the latest insights into clinical and methodological challenges for assessing freezing of gait. We also highlight the role of emerging wearable technology to improve the management of this debilitating symptom.

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Section: Assessing the Presence And Severity Of Fog With Wearable Senmentioning

confidence: 99%

Clinical and methodological challenges for assessing freezing of gait: Future perspectives

Bloem²,

et al. 2019

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“…The smartphone-based sensors with both classical machine learning and deep learning techniques provide the second highest sensitivity, specificity, and accuracy values. Although, both the second-and third-best performing systems in terms of accuracy are Capecci et al [12] and Kim et al [11], which are smartphone-based systems and are highlighted in bold in Table 2 with an accuracy of 93.8% and 92.86%, respectively. It is clear from the table that the proposed system and technique have the highest accuracy and sensitivity (recall) of 99.70% and 97.08%, respectively, compared to other detection systems and classification techniques used for FOG detection.…”

Section: Resultsmentioning

confidence: 99%

“…Currently, various methods using wearable devices and vision-based systems have been exploited for FOG detection. A number of systems have been proposed with wearable sensors or cameras for FOG detection, including (a) wearable accelerometer and/or gyroscope sensors [2,9,10], (b) smartphone-based sensors [11][12][13], (c) electromyograpy sensors [14,15], (d) pressure/force-based sensors [16,17], and (e) vision-based sensors [18,19]. However, such systems suffer from various drawbacks.…”

mentioning

confidence: 99%

WiFreeze: Multiresolution Scalograms for Freezing of Gait Detection in Parkinson’s Leveraging 5G Spectrum with Deep Learning

et al. 2019

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Freezing of Gait (FOG) is an episodic absence of forward movement in Parkinson's Disease (PD) patients and represents an onset of disabilities. FOG hinders daily activities and increases fall risk. There is high demand for automating the process of FOG detection due to its impact on health and well being of individuals. This work presents WiFreeze, a noninvasive, line of sight, and lighting agnostic WiFi-based sensing system, which exploits ambient 5G spectrum for detection and classification of FOG. The core idea is to utilize the amplitude variations of wireless Channel State Information (CSI) to differentiate between FOG and activities of daily life. A total of 225 events with 45 FOG cases are captured from 15 patients with the help of 30 subcarriers and classification is performed with a deep neural network. Multiresolution scalograms are proposed for time-frequency signatures of human activities, due to their ability to capture and detect transients in CSI signals caused by transitions in human movements. A very deep Convolutional Neural Network (CNN), VGG-8K, with 8K neurons each, in fully connected layers is engineered and proposed for transfer learning with multiresolution scalogram features for detection of FOG. The proposed WiFreeze system outperforms all existing wearable and vision-based systems as well as deep CNN architectures with the highest accuracy of 99.7% for FOG detection. Furthermore, the proposed system provides the highest classification accuracies of 94.3% for voluntary stop and 97.6% for walking slow activities, with improvements of 9% and 23%, respectively, over the best performing state-of-the-art deep CNN architecture.China [3]. Every 1 in 37 people in the United Kingdom are diagnosed with PD at some stage of their life [1]. PD is caused by the neuronal loss and damage in the motor area of the brain, particularly the centers of the limbic, visceromotor, and somatomotor systems [4]. Most PD patients experience difficulty in walking, which indicates an onset of gait, balance, and other disabilities [5]. Half of the PD patients experience Freezing of Gate (FOG) symptoms, which represent an episodic absence of forward feet movement, despite of the intention for forward progress [6]. The frequency as well as the duration of FOG episodes increases with the progression of PD. FOG and falls are both interconnected phenomena, and FOG is known to hinder daily activities and increase fall risk [7], resulting in severe consequences for older adults.Conventional clinical methods to validate FOG occurrences and their severity are based on self-report questionnaires [8] and doctors' reports based on direct observation. Due to the impact of FOG on population in general and falls in older adults in particular, there is a high demand for automating the process of detection and recognition of FOG events. Currently, various methods using wearable devices and vision-based systems have been exploited for FOG detection. A number of systems have been proposed with wearable sensors or cameras for FOG detectio...

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“…Smartphones are used as assistants in fitness applications [47,48], heart rate monitoring [49], gait recognition [50], and Human Activity Recognition (HAR) [51][52][53]. The use of smartphones for detecting FOG episodes is proposed in [39,41,54,55]. Experimental protocols encompass timed upand-go on a standardized 5-m course [41,42], walking tasks and turns with or without FOG provocation [44,46,56] or dual tasking (e.g., carrying a full glass of water while walking) [36,39].…”

Section: Introductionmentioning

confidence: 99%

“…In [60], a model based on convolutional neural networks is applied on data from 21 PD patients wearing an IMU on their waist, achieving sensitivity, specificity, and accuracy of 92.6%, 88.0% and 87.0% respectively. In [55], a smartphone is placed in the patient's trouser pocket, and data are represented as 2D images and transferred to a remote server for processing. The methods achieves sensitivity, specificity, accuracy of 93.8% 90.1% and 91.8%, respectively, at the expenses of a quite high computational complexity.…”

Section: Introductionmentioning

confidence: 99%

Home monitoring of motor fluctuations in Parkinson’s disease patients

Borzì

Varrecchia

Olmo

et al. 2019

J Reliable Intell Environ

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In Parkinson's disease, motor fluctuations (worsening of tremor, bradykinesia, freezing of gait, postural instability) affect up to 70% of patients within 9 years of l-dopa therapy. Nevertheless, the assessment of motor fluctuations is difficult in a medical office, and is commonly based on poorly reliable self-reports. Hence, the use of wearable sensors is desirable. In this preliminary trial, we have investigated bradykinesia and freezing of gait-FOG-symptoms by means of inertial measurement units. To this purpose, we have employed a single smartphone on the patient's waist for FOG experiment (38 patients), and on patient thigh for LA (93 subjects). Given the sound performance achieved in this trial (AUC = 0.97 for FOG and AUC = 0.92 for LA), motor fluctuations may be estimated in domestic environments. To this end, we plan to perform measures and data processing on SensorTile, a tiny IoT module including several sensors, a microcontroller, a BlueTooth low-energy interface and microSD card, implementing an electronic diary of motor fluctuations, posture and dyskinesia during activity of daily living.

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Validation of Freezing-of-Gait Monitoring Using Smartphone

Abstract: Using our methodology, the precise and continuous monitoring of freezing events with unconstrained sensing can assist patients in managing their chronic disease in daily life effectively.

Cited by 36 publications

References 25 publications

Clinical and methodological challenges for assessing freezing of gait: Future perspectives

Clinical and methodological challenges for assessing freezing of gait: Future perspectives

WiFreeze: Multiresolution Scalograms for Freezing of Gait Detection in Parkinson’s Leveraging 5G Spectrum with Deep Learning

Home monitoring of motor fluctuations in Parkinson’s disease patients

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