A Waist-Worn Inertial Measurement Unit for Long-Term Monitoring of Parkinson’s Disease Patients

Rodríguez-Martín, Daniel; Pérez-López, Carlos; Samà, Albert; Català, Andreu; Arostegui, J. Manuel Moreno; Cabestany, Joan; Mestre, Berta; Alcaine, Sheila; Prats, Anna; Crespo, María de la Cruz; Bayés, Àngels

doi:10.3390/s17040827

Cited by 43 publications

(39 citation statements)

References 43 publications

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“…In support of this, a recent report suggested that continuous gait monitoring detects higher step variability in PD patients with a positive history of falls compared to non-fallers [ 47 ]. Mobile wearable systems may have the potential to predict the patient’s risk of falling [ 48 ]. This may help facilitate the patient and physician to intervene and adjust treatment before falls occur.…”

Section: Discussionmentioning

confidence: 99%

Wearable sensors objectively measure gait parameters in Parkinson’s disease

et al. 2017

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Distinct gait characteristics like short steps and shuffling gait are prototypical signs commonly observed in Parkinson’s disease. Routinely assessed by observation through clinicians, gait is rated as part of categorical clinical scores. There is an increasing need to provide quantitative measurements of gait, e.g. to provide detailed information about disease progression. Recently, we developed a wearable sensor-based gait analysis system as diagnostic tool that objectively assesses gait parameter in Parkinson’s disease without the need of having a specialized gait laboratory. This system consists of inertial sensor units attached laterally to both shoes. The computed target of measures are spatiotemporal gait parameters including stride length and time, stance phase time, heel-strike and toe-off angle, toe clearance, and inter-stride variation from gait sequences. To translate this prototype into medical care, we conducted a cross-sectional study including 190 Parkinson’s disease patients and 101 age-matched controls and measured gait characteristics during a 4x10 meter walk at the subjects’ preferred speed. To determine intraindividual changes in gait, we monitored the gait characteristics of 63 patients longitudinally. Cross-sectional analysis revealed distinct spatiotemporal gait parameter differences reflecting typical Parkinson’s disease gait characteristics including short steps, shuffling gait, and postural instability specific for different disease stages and levels of motor impairment. The longitudinal analysis revealed that gait parameters were sensitive to changes by mirroring the progressive nature of Parkinson’s disease and corresponded to physician ratings. Taken together, we successfully show that wearable sensor-based gait analysis reaches clinical applicability providing a high biomechanical resolution for gait impairment in Parkinson’s disease. These data demonstrate the feasibility and applicability of objective wearable sensor-based gait measurement in Parkinson’s disease reaching high technological readiness levels for both, large scale clinical studies and individual patient care.

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Section: Discussionmentioning

confidence: 99%

Wearable sensors objectively measure gait parameters in Parkinson’s disease

et al. 2017

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“…Consequently, many undergoing projects are trying to implementing artificial intelligence algorithms for PD symptom-monitoring tasks inside non-intrusive wearable technologies. These systems aim to be suitable for daily addressing this monitoring task [33,34,35,36,37,38]. Within the same trend, state-of-the-art for automatic FOG detection are shallow machine learning (ML) algorithms applied to signals acquired from inertial measurement units (IMU) [39,40,41,35].…”

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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

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178

108

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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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“…The former has an increased power consumption, but a greater transmission speed and distance with respect to the latter. The sample frequency of these sensors varies between 50 and 1000 Hz while the minimum number of bit is 12 [ 106 ].…”

Section: Resultsmentioning

confidence: 99%

Wearable Monitoring Devices for Biomechanical Risk Assessment at Work: Current Status and Future Challenges—A Systematic Review

Ranavolo

Draicchio

Varrecchia

et al. 2018

IJERPH

101

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Background: In order to reduce the risk of work-related musculoskeletal disorders (WMSDs) several methods have been developed, accepted by the international literature and used in the workplace. The purpose of this systematic review was to describe recent implementations of wearable sensors for quantitative instrumental-based biomechanical risk assessments in prevention of WMSDs. Methods: Articles written until 7 May 2018 were selected from PubMed, Scopus, Google Scholar and Web of Science using specific keywords. Results: Instrumental approaches based on inertial measurement units and sEMG sensors have been used for direct evaluations to classify lifting tasks into low and high risk categories. Wearable sensors have also been used for direct instrumental evaluations in handling of low loads at high frequency activities by using the local myoelectric manifestation of muscle fatigue estimation. In the field of the rating of standard methods, on-body wireless sensors network-based approaches for real-time ergonomic assessment in industrial manufacturing have been proposed. Conclusions: Few studies foresee the use of wearable technologies for biomechanical risk assessment although the requirement to obtain increasingly quantitative evaluations, the recent miniaturization process and the need to follow a constantly evolving manual handling scenario is prompting their use.

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A Waist-Worn Inertial Measurement Unit for Long-Term Monitoring of Parkinson’s Disease Patients

Cited by 43 publications

References 43 publications

Wearable sensors objectively measure gait parameters in Parkinson’s disease

Wearable sensors objectively measure gait parameters in Parkinson’s disease

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

Wearable Monitoring Devices for Biomechanical Risk Assessment at Work: Current Status and Future Challenges—A Systematic Review

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