Online FOG Identification in Parkinson's disease with a time-frequency combined Algorithm

Zhao, Yan; Tonn, K.; Niazmand, K.; Fietzek, Urban; D’Angelo, Lorenzo T.; Ceballos-Baumann, Andrés; Lueth, Tim C.

doi:10.1109/bhi.2012.6211542

Cited by 24 publications

(15 citation statements)

References 15 publications

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“…This feature is convenient because it requires only FFT computation. Other feature extraction approaches involve mixed time-frequency features [Zhao et al 2012] and entropy [Tripoliti et al 2013]. In Mazilu et al [2012], the authors investigated the use of time-domain and statistical features, together with FFT features.…”

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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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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“…Within the same year, Zhao et al [55] presented an online threshold-based Within the same year, Rodríguez et al [59] proposed to complement the FOG detection approaches with postural assessment to increase the specificity of the methods. In fact, they were able to increase this metric by about 5% due to preventing the system from detecting FOG events when the patient was not in a standing position.…”

Section: Related Workmentioning

confidence: 99%

Deep learning for freezing of gait detection in Parkinson’s disease patients in their homes using a waist-worn inertial measurement unit

Camps

Sam

Martn

et al. 2018

Knowledge-Based Systems

163

100

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Among Parkinson’s disease (PD) motor symptoms, freezing of gait (FOG) may\ud be the most incapacitating. FOG episodes may result in falls and reduce patients’\ud quality of life. Accurate assessment of FOG would provide objective information\ud to neurologists about the patient’s condition and the symptom’s characteristics,\ud while it could enable non-pharmacologic support based on rhythmic\ud cues.\ud This paper is, to the best of our knowledge, the first study to propose a\ud deep learning method for detecting FOG episodes in PD patients. This model\ud is trained using a novel spectral data representation strategy which considers information from both the previous and current signal windows. Our approach\ud was evaluated using data collected by a waist-placed inertial measurement unit\ud from 21 PD patients who manifested FOG episodes. These data were also employed\ud to reproduce the state-of-the-art methodologies, which served to perform\ud a comparative study to our FOG monitoring system.\ud The results of this study demonstrate that our approach successfully outperforms\ud the state-of-the-art methods for automatic FOG detection. Precisely, the\ud deep learning model achieved 90% for the geometric mean between sensitivity\ud and specificity, whereas the state-of-the-art methods were unable to surpass the\ud 83% for the same metric.Peer ReviewedPostprint (published version

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“…In fact, more than a third of PD patients older than 65 suffer at least one fall per year, representing 65% of all their injuries. As a consequence, PD patients develop increasing fear of falling, which causes stress and produces a significant psychological impact on their lives [32].…”

Section: Parkinson’s Diseasementioning

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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Online FOG Identification in Parkinson's disease with a time-frequency combined Algorithm

Cited by 24 publications

References 15 publications

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

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

Deep learning for freezing of gait detection in Parkinson’s disease patients in their homes using a waist-worn inertial measurement unit

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

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