Gait monitoring system for patients with Parkinson’s disease

Gonçalves, Helena R.; Rodrigues, Margarida; Santos, Cristina Paludo

doi:10.1016/j.eswa.2021.115653

Cited by 15 publications

(8 citation statements)

References 41 publications

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“…Gait-associated metrics were estimated based on a pre-identi cation of initial/ nal contact detection using the gait events detection algorithm validated by the authors team in [19]. In addition, the computational method used to estimate most of these metrics was previously validated in [19] showing an acceptable error of ≤ 13,45%, and tested in [21] describing the expected parkinsonian prototypical motor signs.…”

Section: Discussionmentioning

confidence: 99%

“…In addition, the computational method used to estimate most of these metrics was previously validated in [19] showing an acceptable error of ≤ 13,45%, and tested in [21] describing the expected parkinsonian prototypical motor signs. Given patients with higher postural instability tend to present a more cautious behaviour in performing the tasks, which leads to a slower and smaller steps, it is expected a decrease in step/stride length, velocity, cadence and swing time and, in turn, an increase in step/stride time and stance and double support time [8, 9,11,19]. In addition, gait asymmetry (AS) and standard deviation (SD) are higher with progression of postural instability.…”

Section: Discussionmentioning

confidence: 99%

“…1. The participants wore the sensBand which comprises the following systems: (1) a sensory acquisition system, responsible for collect acceleration and angular velocity data through an inertial measurement unit (MPU-6050); (2) a processing system, which consists of a STM32F4-Discovery responsible for receiving the data acquired and detecting gait events (heel-strike, foot-at, mid-stance, toe-off and heel-off) using an algorithm based on heuristic rules with adaptive thresholds and ranges to segment the gait cycle of both legs [19]; (3) a data storage system, based on an OTG USB driver to store the inertial data and the gait events identi ed; (4) a mobile A1P, which relies on an Android APP that connects to the processing unit wirelessly (via Bluetooth) in order to start/stop data acquisition, manage the operability settings and plot the acquired data; and (5) a desktop APP which loads, process and estimate several gait and postural-associated metrics. sensBand was previously validated through a benchmarking analysis with a well-known ad validated commercial system, where non-statistical differences were measured between both systems) Also, the system showed to be able to estimate gait events required for gait and postural-associated metrics with an sensitivity of 99,53% and accuracy of 97,42% [19].…”

Section: Methodsmentioning

confidence: 99%

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Objective assessment of postural instability in Parkinson’s disease under dynamic conditions

Cardoso

Gonçalves

Rodrigues

et al. 2023

Preprint

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Postural instability is considered one of the most incapacitating motor symptoms and a primary cause of falls in Parkinson’s disease (PD), compromising the autonomy and well-being of people suffering from this condition. Usually, the clinical examination used to evaluate this symptom is the pull test since it does not require any instruments and is easy to perform. However, this test is difficult to standardize and is not sensitive to subtle but significant changes. Recent studies have shown that inertial measurement units constitute a portable and cost-effective solution to measure postural sway, which provides metrics sensitive to balance disorders as postural instability. Further studies are required to monitor patients’ postural conditions and implement an adequate treatment to improve postural stability, it is crucial to quantify balance under dynamic conditions. Hence, the proposed research focused on investigating the hypothesis of whether it is possible to differentiate between all the scores of the pull test through postural and gait metrics extracted from raw acceleration and angular velocity signals from the center of mass of patients with PD acquired while performing basic day-to-day tasks. A cross-sectional study was followed, including 23 patients, and the achieved results showed that most of the estimated metrics can differentiate the pull test scores (ρ-value≤0.048, R2≥0.513). Overall, satisfactory results were achieved as the statistical analysis revealed gait and postural metrics estimated under dynamic conditions were considered relevant to distinguish between the scores of the pull test.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

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Objective assessment of postural instability in Parkinson’s disease under dynamic conditions

Cardoso

Gonçalves

Rodrigues

et al. 2023

Preprint

Self Cite

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“…Jarchi et al ( 2014 ) explored the use of a single ear-worn IMU for gait analysis, and they demonstrated promising results in gait event detection. Recent studies leveraged multiple IMUs, such as Xsens Dot (XSens, Enschede, The Netherlands), attached to lower limbs to recover the 3D skeleton during walking (Gonçalves et al, 2021 ).…”

Section: Gait Analysis Methodologymentioning

confidence: 99%

Detection and assessment of Parkinson's disease based on gait analysis: A survey

Guo

Yang

Liu

et al. 2022

Front. Aging Neurosci.

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Neurological disorders represent one of the leading causes of disability and mortality in the world. Parkinson's Disease (PD), for example, affecting millions of people worldwide is often manifested as impaired posture and gait. These impairments have been used as a clinical sign for the early detection of PD, as well as an objective index for pervasive monitoring of the PD patients in daily life. This review presents the evidence that demonstrates the relationship between human gait and PD, and illustrates the role of different gait analysis systems based on vision or wearable sensors. It also provides a comprehensive overview of the available automatic recognition systems for the detection and management of PD. The intervening measures for improving gait performance are summarized, in which the smart devices for gait intervention are emphasized. Finally, this review highlights some of the new opportunities in detecting, monitoring, and treating of PD based on gait, which could facilitate the development of objective gait-based biomarkers for personalized support and treatment of PD.

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“…Many scientists advocated that all patients with degenerative disorders and those in need of long-term rehabilitation should undergo gait analysis that requires the use of human gait monitoring instruments [ 1 ]. Due to the lack of proper instruments, data related to monitoring gait disabilities and patient motility are difficult to collect for clinicians and hospitals [ 2 ]. In this work, the main aim is to develop a sensor for analyzing human locomotion.…”

Section: Introductionmentioning

confidence: 99%

Silk Fibroin-Based Piezoelectric Sensor with Carbon Nanofibers for Wearable Health Monitoring Applications

Rathinasamy

Maheswar

Lörincz

2023

Sensors

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The continuous real-time monitoring of human health using biomedical sensing devices has recently become a promising approach to the realization of distant health monitoring. In this paper, the piezoelectric characteristics of the silk fibroin (SF) natural polymer were analyzed as the material used for obtaining sensing information in the application of distance health monitoring. To enhance the SF piezoelectricity, this paper presents the development of a novel SF-based sensor realized by combining SF with different carbon nanofiber (CNF) densities, and for such newly developed SF-based sensors comprehensive performance analyses have been performed. Versatile methods including the scanning electron microscope, Fourier transform infrared spectroscopy, Raman and X-ray diffraction measurements and impedance analysis were used to study the morphologic, mechanical and electrical properties of the developed SF-based sensor. The SF with CNF samples was analyzed for three different pressure loads (40 N, 60 N and 80 N) in 500 compression test cycles. The analyses thoroughly describe how combining natural polymer SF with different CNF densities impacts the piezoelectricity and mechanical strength of the proposed SF-based sensor. The developed piezoelectric SF-based sensors were further tested on humans in real medical applications to detect generated piezoelectric voltage in versatile body movements. The maximum piezoelectricity equal to 2.95 ± 0.03 V was achieved for the jumping movement, and the SF sample with a CNF density equal to 0.4% was tested. Obtained results also show that the proposed SF-based sensor has an appropriate piezoelectric sensitivity for each of the analyzed body movement types, and that the proposed SF-based sensor can be applied in real medical applications as a biomedical sensing device. The proposed SF-based sensor’s practical implementation is further confirmed by the results of cytotoxicity analyses, which show that the developed sensor has a non-toxic and biocompatible nature and can be efficiently used in skin contact for biomedical wearable health monitoring applications.

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Gait monitoring system for patients with Parkinson’s disease

Cited by 15 publications

References 41 publications

Objective assessment of postural instability in Parkinson’s disease under dynamic conditions

Objective assessment of postural instability in Parkinson’s disease under dynamic conditions

Detection and assessment of Parkinson's disease based on gait analysis: A survey

Silk Fibroin-Based Piezoelectric Sensor with Carbon Nanofibers for Wearable Health Monitoring Applications

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