Automatic detection of freezing of gait events in patients with Parkinson's disease

Tripoliti, Evanthia E.; Tzallas, Alexandros T.; Tsipouras, Markos G.; Rigas, George; Bougia, Panagiota; Leontiou, Michael; Konitsiotis, Spiros; Chondrogiorgi, Maria; Tsouli, Sofia; Fotiadis, Dimitrios I.

doi:10.1016/j.cmpb.2012.10.016

Cited by 174 publications

(139 citation statements)

References 25 publications

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“…Machine learning models have been proven to be useful in accurately detecting human activities such as walking, sitting, standing, and falling [Bulling et al 2014;Choi et al 2011]. A couple of recent studies show that FoG can be accurately detected with machine learning models and wearable sensor data [Mazilu et al 2012;Tripoliti et al 2013]. …”

Section: System Architecturementioning

confidence: 99%

“…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%

“…We choose the ankle position for the wearable sensors to be attached based on previous studies [Bächlin et al 2010;Tripoliti et al 2013;Mazilu et al 2012], but also from our prior analysis [Mazilu et al 2014b], which shows that inertial data from the ankle position gives the best information to characterize FoG and distinguish it from other types of human gait. Moreover, in Mazilu et al [2014a], the ankle position received the highest wearability scores to attach on-body sensors for a wearable assistant.…”

Section: System Architecturementioning

confidence: 99%

“…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%

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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: System Architecturementioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: System Architecturementioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

See 2 more Smart Citations

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.

View full text Add to dashboard Cite

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“…The basic signs which define PD diagnosis are: tremor, usually beginning in a limb at rest, and become less prominent with voluntary movement [13]; rigidity, muscle stiffness may occur in any part of the body, and can limit patients range of motion and may cause pain; and bradykinesia (slowness of movement) [14], the disease may reduce the ability to move and slows the movement, steps become shorter when walking [15], and patients may find it difficult to get up from a chair. The postural instability and freezing of gait (FoG) [16] [17], which is one of main causes of falls in PD, occurs later as the disease progresses. Other symptoms like dysphagia (swallowing disturbance) or handwriting impairment are common motor problems which can appear.…”

Section: A Parkinson's Diseasementioning

confidence: 99%

A Multi-agent Architecture for Ontology-Based Diagnosis of Mental Disorders

Ivașcu

Manațe

Negru

2015

2015 17th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing (SYNASC)

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This paper presents a Multi-agent system that facilitates the remote monitoring of the elderly patients which are susceptible to mental disorder diseases. In order to find early signs of health condition depreciation we have assessed four of the most common mental disorder diseases to find which kind of sensors can detect specific symptoms with the main purpose of creating an early warning system. The diagnosis component is based on an ontology that defines the relations between sensors, symptoms and diseases. Based on these relationships a specialized agent can inform the medical personnel about the detected symptoms.

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Analysis of gait characteristics and related factors in patients with Parkinson's disease based on wearable devices

Tang,

Liao,

Yao

et al. 2024

Brain and Behavior

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BackgroundPostural instability and gait disorder dominant (PIGD) is one of the most common disabling symptoms of Parkinson's disease (PD), which seriously affects patients’ quality of life. Therefore, it is essential to identify PIGD and develop targeted interventions to reduce the risk of PIGD in PD patients.Aim:This study aimed to investigate the gait characteristics of PD patients based on wearable devices and to establish a predictive model for their related influencing factors.MethodsThe retrospective medical records of patients from January 2020 to September 2023 were collected, including 159 patients with PD (divided into PIGD [n = 73] and non‐PIGD [n = 86] groups) and 200 healthy patients (as the healthy control group). Information from social demographic data, a blood test, scale scores, gait analysis based on wearable devices, white matter lesions, and the Fazekas scale was extracted and analyzed.ResultsCompared with the healthy control group, the mean step length, mean rate, mean angular velocity, and step length were lower in the PD group, while the mean steps were higher in the turning test. The incidence of PIGD was 46% in PD patients, and PD patients with the non‐tremor onset mode were more likely to develop PIGD than those with the tremor onset mode. Compared to the non‐PIGD group, the PIGD group showed more serious gait problems in different experimental tasks and had a higher Hoehn and Yahr (H‐Y) stage, Hamilton Anxiety Scale (HAMA) score, Hamilton Depression Scale score, periventricular white matter (PVWM) score, deep white matter score, and Fazekas scale score, but they had lower hemoglobin levels, D‐dimer levels, Tinetti Balance scores, Tinetti Gait scores, Berg Balance Scale scores, and Mini‐Mental State Examination (MMSE) scores. Logistic regression analysis showed that the MMSE score was negatively correlated with the occurrence of PIGD, while the HAMA score, H‐Y stage, PVWM score, and non‐tremor form of onset were positively correlated with the occurrence of PIGDConclusionThe incidence of gait disorder in PD patients is higher than that in the normal population. Moreover, cognitive dysfunction, anxiety state, H‐Y stage, PVWM score, and the non‐tremor mode of onset can be considered independent risk factors for PIGD.

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Automatic detection of freezing of gait events in patients with Parkinson's disease

Cited by 174 publications

References 25 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

A Multi-agent Architecture for Ontology-Based Diagnosis of Mental Disorders

Analysis of gait characteristics and related factors in patients with Parkinson's disease based on wearable devices

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