Toward a Wearable System for Predicting Freezing of Gait in People Affected by Parkinson's Disease

Demrozi, Florenc; Bacchin, Ruggero; Tamburin, Stefano; Cristani, Marco; Pravadelli, Graziano

doi:10.1109/jbhi.2019.2952618

Cited by 59 publications

(41 citation statements)

References 28 publications

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“…Inertial wearable sensors also enable monitoring of spatial-temporal gait degradation in patients with FOG, possibly useful for the recognition of pre-FOG periods. Indeed, their usage, in combination with machine learning (ML) analysis, has recently smoothed the path for FOG prediction [29][30][31][32][33][34]. By examining several time and frequency-domain gait features, these studies have achieved the real-time detection of pre-FOG periods [29,31,32,35].…”

Section: Introductionmentioning

confidence: 99%

Prediction of Freezing of Gait in Parkinson’s Disease Using Wearables and Machine Learning

Borzì

Mazzetta

Zampogna

et al. 2021

Sensors

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Freezing of gait (FOG) is one of the most troublesome symptoms of Parkinson’s disease, affecting more than 50% of patients in advanced stages of the disease. Wearable technology has been widely used for its automatic detection, and some papers have been recently published in the direction of its prediction. Such predictions may be used for the administration of cues, in order to prevent the occurrence of gait freezing. The aim of the present study was to propose a wearable system able to catch the typical degradation of the walking pattern preceding FOG episodes, to achieve reliable FOG prediction using machine learning algorithms and verify whether dopaminergic therapy affects the ability of our system to detect and predict FOG. Methods: A cohort of 11 Parkinson’s disease patients receiving (on) and not receiving (off) dopaminergic therapy was equipped with two inertial sensors placed on each shin, and asked to perform a timed up and go test. We performed a step-to-step segmentation of the angular velocity signals and subsequent feature extraction from both time and frequency domains. We employed a wrapper approach for feature selection and optimized different machine learning classifiers in order to catch FOG and pre-FOG episodes. Results: The implemented FOG detection algorithm achieved excellent performance in a leave-one-subject-out validation, in patients both on and off therapy. As for pre-FOG detection, the implemented classification algorithm achieved 84.1% (85.5%) sensitivity, 85.9% (86.3%) specificity and 85.5% (86.1%) accuracy in leave-one-subject-out validation, in patients on (off) therapy. When the classification model was trained with data from patients on (off) and tested on patients off (on), we found 84.0% (56.6%) sensitivity, 88.3% (92.5%) specificity and 87.4% (86.3%) accuracy. Conclusions: Machine learning models are capable of predicting FOG before its actual occurrence with adequate accuracy. The dopaminergic therapy affects pre-FOG gait patterns, thereby influencing the algorithm’s effectiveness.

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Section: Introductionmentioning

confidence: 99%

Prediction of Freezing of Gait in Parkinson’s Disease Using Wearables and Machine Learning

Borzì

Mazzetta

Zampogna

et al. 2021

Sensors

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“…One main field of HAR research is population ageing and the increase in the number of persons with physical and cognitive impairments. Many HAR models are used to assist users in identifying and preventing risks such as falling in older adults in parkinson's disease [41]- [43] or freezing gait (foG) [24]. In addition, ADLs are becoming common for activity tracking devices.…”

Section: B Human Activitymentioning

confidence: 99%

“…HAR algorithms are mainly aimed at acknowledging human activity based on data collected from wearable and environmental sensors [13], [24]. These behaviours are primarily recognised on the basis of CML and DL.…”

Section: Introductionmentioning

confidence: 99%

Human Activity Recognition with Deep Learning: Overview, Challenges & Possibilities

Kumar¹

2021

Preprint

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Human Activity Recognition (HAR) has become a vibrant research field over the last decade, especially because of the spread of electronic devices like mobile phones, smart cell phones, and video cameras in our daily lives. In addition, the progress of deep learning and other algorithms has made it possible for researchers to use HAR in many fields including sports, health, and well-being. HAR is, for example, one of the most promising resources for helping older people with the support of their cognitive and physical function through day-to-day activities. This study focuses on the key role machine learning plays in the development of HAR applications. While numerous HAR surveys and review articles have previously been carried out, the main/overall HAR issue was not taken into account, and these studies concentrate only on specific HAR topics. A detailed review paper covering major HAR topics is therefore essential. This study analyses the most up-to-date studies on HAR in recent years and provides a classification of HAR methodology and demonstrates advantages and disadvantages for each group of methods. This paper finally addresses many problems in the current HAR subject and provides recommendations for potential study.

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“…One main field of HAR research is population ageing and the increase in the total number of persons with physical and cognition affliction. Many activity recognition models are being deployed in order to assist individuals in identifying along with preventing risks as that of falling in older adults in parkinson's disease [60]- [62] or freezing gait (foG) [34]. In addition, ADLs are becoming common for activity tracking devices.…”

Section: Human Activitymentioning

confidence: 99%

“…HAR algorithms are mainly aimed at acknowledging activities based on data collected from wearables along with environmental sensors [23], [34]. These behaviours are primarily recognised on the basis of Machine Learning(ML) and Deep Learning(DL) techniques.…”

Section: Introductionmentioning

confidence: 99%

Human Activity Recognition with Deep Learning: Methods, Progress & Possibilities

Kumar¹

2021

Preprint

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Over the past decade, recognition of human activities (HAR) has become a vibrant field of research, in particular, the spread in our everyday lives of electronics such as mobile phones, smart cell phones, and video cameras. Furthermore, the advancement in the field of deep methodologies and other paradigms have enabled scientists to enable HAR in many areas, consisting of activities in fitness and wellness. For instance, HAR is one of many resorts to support older people through day-to-day activities to support their cognition and physicality. This study is centered on the key aspects deep learning plays in the development of HAR applications. Although numerous HAR examination studies were carried out previously, there have been no overall studies on this subject, in all the earlier studies there were only specific HAR-related subjects. A detailed review covering all the main subjects in this area is therefore essential. This study discusses the latest developments and works in HAR. It separates the methods and the advantages and disadvantages of each method group. This paper finally addresses many problems in the current HAR subject and provides recommendations for potential study.

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Toward a Wearable System for Predicting Freezing of Gait in People Affected by Parkinson's Disease

Cited by 59 publications

References 28 publications

Prediction of Freezing of Gait in Parkinson’s Disease Using Wearables and Machine Learning

Prediction of Freezing of Gait in Parkinson’s Disease Using Wearables and Machine Learning

Human Activity Recognition with Deep Learning: Overview, Challenges & Possibilities

Human Activity Recognition with Deep Learning: Methods, Progress & Possibilities

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