Quantification of whole-body bradykinesia in Parkinson's disease participants using multiple inertial sensors

Memar, Sara; Delrobaei, Mehdi; Pieterman, Marcus; McIsaac, Kenneth; Jog, Mandar

doi:10.1016/j.jns.2018.02.001

Cited by 38 publications

(18 citation statements)

References 33 publications

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“…Accelerometers, gyroscopes and magnetometers can be the component of the same inertial measurement unit (IMU) device ( Figure 5), one of the most widely used type of sensors in gait analysis especially in PD [33,53]. It can measure velocity, acceleration, orientation and gravitational forces and can be used to study gait initiation [54], assess standing balance [55] and quantify bradykinesia [56]. Accelerometers are composed of a mechanical sensing element with a proof mass attached to a mechanical suspension system, with respect to a reference frame, that can be forced to deflect by the inertial force according to the acceleration of gravity.…”

Section: Gait Analysis Technologiesmentioning

confidence: 99%

Gait Analysis in Parkinson’s Disease: An Overview of the Most Accurate Markers for Diagnosis and Symptoms Monitoring

Biase

Santo

Caminiti

et al. 2020

Sensors

124

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The aim of this review is to summarize that most relevant technologies used to evaluate gait features and the associated algorithms that have shown promise to aid diagnosis and symptom monitoring in Parkinson’s disease (PD) patients. We searched PubMed for studies published between 1 January 2005, and 30 August 2019 on gait analysis in PD. We selected studies that have either used technologies to distinguish PD patients from healthy subjects or stratified PD patients according to motor status or disease stages. Only those studies that reported at least 80% sensitivity and specificity were included. Gait analysis algorithms used for diagnosis showed a balanced accuracy range of 83.5–100%, sensitivity of 83.3–100% and specificity of 82–100%. For motor status discrimination the gait analysis algorithms showed a balanced accuracy range of 90.8–100%, sensitivity of 92.5–100% and specificity of 88–100%. Despite a large number of studies on the topic of objective gait analysis in PD, only a limited number of studies reported algorithms that were accurate enough deemed to be useful for diagnosis and symptoms monitoring. In addition, none of the reported algorithms and technologies has been validated in large scale, independent studies.

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Section: Gait Analysis Technologiesmentioning

confidence: 99%

Gait Analysis in Parkinson’s Disease: An Overview of the Most Accurate Markers for Diagnosis and Symptoms Monitoring

Biase

Santo

Caminiti

et al. 2020

Sensors

124

View full text Add to dashboard Cite

show abstract

“…[34]) or Parkinson's disease (e.g. [35], [36]) of affected patients as well as posture/spine disorder for healthy patients (e.g. [37]).…”

Section: Prevalent Application Scenarios Could Be Identifiedmentioning

confidence: 99%

Transforming Personal Healthcare through Technology - A Systematic Literature Review of Wearable Sensors for Medical Application

Witte¹,

Zarnekow²

2019

Proceedings of the Annual Hawaii International Conference on System Sciences

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Wearable Sensor Health Technology (WSHT) captures, analyzes and aggregates physiological data to improve personal well-being. Recently the technology market is flooded with wearable sensors that measure health-related data and have a high user adoption. Nevertheless, these devices are almost exclusively used for fitness purposes and the healthcare sector still faces the challenge of constantly increasing costs. To respond to the necessary but rare use of WSHT in professional healthcare, we aim to identify the most promising areas for future medical implementation. Therefore, we performed a systematic literature search and reviewed 97 papers with regard to disease treatment, application area, vital parameter measurement and target patient. As a result, we could identify five potential areas for further research: (RA1) concentration on widespread diseases, (RA2) expansion of WSHT's functionality, (RA3) diversity of vital parameter measurements, (RA4) proactive analysis of sensor data for preventive purposes and (RA5) promoting patient adoption through enhanced usability.

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“…Some studies aimed to develop sensor technology that allows quantification of motor states including Off, On and On with dyskinesia [11], [12], [13]. Promising results for predicting PD symptom severity with regard to UPDRS [14] and quantifying whole-body bradykinesia [15] were reported using multimodal sensors. A review study performed by Rovini et al [8] reported that there are a small number of studies that used sensor data from lower extremities.…”

Section: Introductionmentioning

confidence: 99%

Motion Sensor-Based Assessment of Parkinson's Disease Motor Symptoms During Leg Agility Tests: Results From Levodopa Challenge

Aghanavesi

Bergquist

Nyholm

et al. 2020

IEEE J. Biomed. Health Inform.

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Quantification of whole-body bradykinesia in Parkinson's disease participants using multiple inertial sensors

Cited by 38 publications

References 33 publications

Gait Analysis in Parkinson’s Disease: An Overview of the Most Accurate Markers for Diagnosis and Symptoms Monitoring

Gait Analysis in Parkinson’s Disease: An Overview of the Most Accurate Markers for Diagnosis and Symptoms Monitoring

Transforming Personal Healthcare through Technology - A Systematic Literature Review of Wearable Sensors for Medical Application

Motion Sensor-Based Assessment of Parkinson's Disease Motor Symptoms During Leg Agility Tests: Results From Levodopa Challenge

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