Smart Sensing Systems for the Detection of Human Motion Disorders

Lorenzi, Paolo; Rao, Rosario; Romano, Giulio; Kita, Ardian; Serpa, M.; Filesi, F.; Parisi, Raffaele; Suppa, Antonio; Bologna, Matteo; Berardelli, Alfredo; Irrera, Fernanda

doi:10.1016/j.proeng.2015.08.626

Cited by 15 publications

(11 citation statements)

References 14 publications

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“…In the 20 studies included in this review, IMUs were used alone, with other sensors, or integrated in different technological devices to improve the detection of FOG events (Table 4 ). Force sensors (Djurić-Jovičić et al, 2014b ), EMG (Cole et al, 2011 ), headsets (Lorenzi et al, 2015 ), earphones (Bächlin et al, 2009 , 2010 ), ECG and Galvanic Skin Response (GSR) sensors (Mazilu et al, 2015 ), and a portable four-channel wireless electroencephalogram (EEG) system (Handojoseno et al, 2012 , 2013 , 2014 , 2015 ) were the most common supplementary devices used to provide biofeedback. In contrast, Morris et al ( 2013 ) proposed a validated method to assess the phenomenon using a computer-generated animation and reconstructed data coming from IMUs.…”

Section: Resultsmentioning

confidence: 99%

“…An interesting challenge would be freezing prediction instead of freezing detection (Mazilu et al, 2015 ), whereas possible integration of IMUs with other sensors to measure physiological parameters could provide a more complete analysis of patients' status related to the detection and prevention of FOG episodes, even if current results have limited accuracy (Handojoseno et al, 2012 , 2013 , 2014 , 2015 ; Mazilu et al, 2015 ). Finally, the system should be usable outdoors, during unconstrained and unscripted activities (Cole et al, 2011 ), and be highly compact (Lorenzi et al, 2015 ), unobtrusive, light weight, easy to use, and meet the requirements of acceptability (Tripoliti et al, 2013 ; Capecci et al, 2016 ). In this direction, a smartphone-based system (Capecci et al, 2016 ) could be a valid solution that could allow patients to use the system during everyday activities and in the community, without discomfort.…”

Section: Resultsmentioning

confidence: 99%

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How Wearable Sensors Can Support Parkinson's Disease Diagnosis and Treatment: A Systematic Review

Maremmani²,

2017

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Background: Parkinson's disease (PD) is a common and disabling pathology that is characterized by both motor and non-motor symptoms and affects millions of people worldwide. The disease significantly affects quality of life of those affected. Many works in literature discuss the effects of the disease. The most promising trends involve sensor devices, which are low cost, low power, unobtrusive, and accurate in the measurements, for monitoring and managing the pathology. Objectives: This review focuses on wearable devices for PD applications and identifies five main fields: early diagnosis, tremor, body motion analysis, motor fluctuations (ON–OFF phases), and home and long-term monitoring. The concept is to obtain an overview of the pathology at each stage of development, from the beginning of the disease to consider early symptoms, during disease progression with analysis of the most common disorders, and including management of the most complicated situations (i.e., motor fluctuations and long-term remote monitoring). Data sources: The research was conducted within three databases: IEEE Xplore®, Science Direct®, and PubMed Central®, between January 2006 and December 2016. Study eligibility criteria: Since 1,429 articles were found, accurate definition of the exclusion criteria and selection strategy allowed identification of the most relevant papers. Results: Finally, 136 papers were fully evaluated and included in this review, allowing a wide overview of wearable devices for the management of Parkinson's disease.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

How Wearable Sensors Can Support Parkinson's Disease Diagnosis and Treatment: A Systematic Review

Maremmani²,

2017

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“…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%

“…In this method, model training used data from all but one participant, model testing used data from the remaining participant, the process was repeated for each participant, and the performance results were averaged. Other studies, often more preliminary in nature, used ad hoc optimization to tune parameters and set thresholds [34,44,48,59,60,61,62,63,95]. This approach, although useful for initial system assessment, is not a good indicator of classifier performance, and should be followed by a more robust evaluation scheme, such as cross-validation.…”

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

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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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“…Basic video is captured using two or more cameras with a known focal length at a fixed distance from the subject. Results obtained from all cameras need calibration to give corrected results [9].…”

Section: B Video Camerasmentioning

confidence: 99%

Multi-modality fusion of floor and ambulatory sensors for gait classification

Yunas

Alharthi

Ozanyan

2019

2019 IEEE 28th International Symposium on Industrial Electronics (ISIE)

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In a case study of gait classification from floor and ambulatory sensors, we compare results with data from each modality. The automatic extraction of features is achieved by Principle Component Analysis or Canonical Correlation Analysis, the latter performing better even with a reduced number of components used. Non-linear classifiers are most efficient for fused features. With a Kernel Support Vector Machine around 94% accuracy is demonstrated, improving over the 87% and 79% accuracies obtained with separate floor and ambulatory sensor data, respectively.

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Smart Sensing Systems for the Detection of Human Motion Disorders

Cited by 15 publications

References 14 publications

How Wearable Sensors Can Support Parkinson's Disease Diagnosis and Treatment: A Systematic Review

How Wearable Sensors Can Support Parkinson's Disease Diagnosis and Treatment: A Systematic Review

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

Multi-modality fusion of floor and ambulatory sensors for gait classification

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