Wearable Sensor Based Stooped Posture Estimation in Simulated Parkinson’s Disease Gaits

Dang, Quoc Khanh; Seo, Han Gil; Pham, Duy Duong; Chee, Youngjoon

doi:10.3390/s19020223

Cited by 12 publications

(6 citation statements)

References 30 publications

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“…For Parkinson's disease (PD), several approaches have been developed for wearable monitoring systems during the last years, with detection of bradykinesia and tremor [97][98][99][100][101]. Machine learning has been used to detect circadian rhythms and sleep, motor, and autonomic disruption, which is suitable for objective and non-invasive monitoring of PD patients [98].…”

Section: A B Cmentioning

confidence: 99%

Microfluidic Point-of-Care Devices: New Trends and Future Prospects for eHealth Diagnostics

Mejía‐Salazar

Cruz

Vásques

et al. 2020

Sensors

138

111

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Point-of-care (PoC) diagnostics is promising for early detection of a number of diseases, including cancer, diabetes, and cardiovascular diseases, in addition to serving for monitoring health conditions. To be efficient and cost-effective, portable PoC devices are made with microfluidic technologies, with which laboratory analysis can be made with small-volume samples. Recent years have witnessed considerable progress in this area with “epidermal electronics”, including miniaturized wearable diagnosis devices. These wearable devices allow for continuous real-time transmission of biological data to the Internet for further processing and transformation into clinical knowledge. Other approaches include bluetooth and WiFi technology for data transmission from portable (non-wearable) diagnosis devices to cellphones or computers, and then to the Internet for communication with centralized healthcare structures. There are, however, considerable challenges to be faced before PoC devices become routine in the clinical practice. For instance, the implementation of this technology requires integration of detection components with other fluid regulatory elements at the microscale, where fluid-flow properties become increasingly controlled by viscous forces rather than inertial forces. Another challenge is to develop new materials for environmentally friendly, cheap, and portable microfluidic devices. In this review paper, we first revisit the progress made in the last few years and discuss trends and strategies for the fabrication of microfluidic devices. Then, we discuss the challenges in lab-on-a-chip biosensing devices, including colorimetric sensors coupled to smartphones, plasmonic sensors, and electronic tongues. The latter ones use statistical and big data analysis for proper classification. The increasing use of big data and artificial intelligence methods is then commented upon in the context of wearable and handled biosensing platforms for the Internet of things and futuristic healthcare systems.

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Section: A B Cmentioning

confidence: 99%

Microfluidic Point-of-Care Devices: New Trends and Future Prospects for eHealth Diagnostics

Mejía‐Salazar

Cruz

Vásques

et al. 2020

Sensors

138

111

View full text Add to dashboard Cite

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“…Therefore, N was chosen as 20 to eliminate the movement artifacts in each sensor axis since the sensor samples at 20 Hz. This measurement has been validated in healthy participants by comparing it with a 3D camera system with markers (Optitrack), with mean absolute errors of 0.9-1.5 • [18].…”

Section: Measurement Of Angles and Vibration Feedbackmentioning

confidence: 99%

“…Furthermore, there have been other attempts to use wearable sensors for home-based gait training in patients with PD [16,17]. Our previous study showed that measurement of the forward flexion angles of the neck and back using wearable sensors is a valid method compared with the use of a 3D camera system during simulated parkinsonian gait in healthy volunteers [18].…”

Section: Introductionmentioning

confidence: 99%

Measurement and Correction of Stooped Posture during Gait Using Wearable Sensors in Patients with Parkinsonism: A Preliminary Study

Kim

Yun

Dang

et al. 2021

Sensors

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Stooped posture, which is usually aggravated during walking, is one of the typical postural deformities in patients with parkinsonism. However, the degree of stooped posture is difficult to quantitatively measure during walking. Furthermore, continuous feedback on posture is also difficult to provide. The purpose of this study is to measure the degree of stooped posture during gait and to investigate whether vibration feedback from sensor modules can improve a patient’s posture. Parkinsonian patients with stooped posture were recruited for this study. Two wearable sensors with three-axis accelerometers were attached, one at the upper neck and the other just below the C7 spinous process of the patients. After being calibrated in the most upright posture, the sensors continuously recorded the sagittal angles at 20 Hz and averaged the data at every second during a 6 min walk test. In the control session, the patients walked with the sensors as usual. In the vibration session, sensory feedback was provided through vibrations from the neck sensor module when the sagittal angle exceeded a programmable threshold value. Data were collected and analyzed successfully in a total of 10 patients. The neck flexion and back flexion were slightly aggravated during gait, although the average change was <10° in most patients in both measurement sessions. Therefore, it was difficult to evaluate the effect of sensory feedback through vibration. However, some patients showed immediate response to the feedback and corrected their posture during gait. In conclusion, this preliminary study suggests that stooped posture could be quantitatively measured during gait by using wearable sensors in patients with parkinsonism. Sensory feedback through vibration from sensor modules may help in correcting posture during gait in selected patients.

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“…38 In Parkinson disease, gait pattern has been monitored via sensor built-in shoes or wearable devices. 43 Gait disturbance is an important sign of compressive myelopathy. 44,45 If gait is monitored in real time by sensors mounted on shoes or wearable devices, it is possible to promptly diagnose the progression of myelopathy in patients with cervical stenosis or ossification of posterior longitudinal ligament.…”

Section: Internet Of Things and Digital Biomarkers Associated With Spinementioning

confidence: 99%

Internet of Things, Digital Biomarker, and Artificial Intelligence in Spine: Current and Future Perspectives

et al. 2019

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Recent interest in medical artificial intelligence (AI) has increased with onset of the fourth industrial revolution. Real-time monitoring of patients is an important research area of medical AI. The medical AI is very closely related to the Internet of Things (IoT), a core element of the fourth industrial revolution. Attempts to diagnose and treat patients using IoT have been already applied to patients with chronic disease such as hypertension and arrhythmia. However, in the spine, research on IoT and digital biomarkers are still in the early stages. The digital biomarker obtained by IoT devices is objective and could represent real-time, real-world, and abundant data. Based on its characteristics, IoT and digital biomarkers can also be useful in the spine. Currently, research on real-time monitoring of physical activity or spinal posture is ongoing. Therefore, the authors introduce the basic concepts of IoT and digital biomarkers, their relationship to AI, and recent trends. Current and future perspectives of IoT and digital biomarker in spine are also discussed. In the future, it is expected that IoT, digital biomarkers, and AI will lead to a paradigm shift in the diagnosis and treatment of spinal diseases.

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Wearable Sensor Based Stooped Posture Estimation in Simulated Parkinson’s Disease Gaits

Cited by 12 publications

References 30 publications

Microfluidic Point-of-Care Devices: New Trends and Future Prospects for eHealth Diagnostics

Microfluidic Point-of-Care Devices: New Trends and Future Prospects for eHealth Diagnostics

Measurement and Correction of Stooped Posture during Gait Using Wearable Sensors in Patients with Parkinsonism: A Preliminary Study

Internet of Things, Digital Biomarker, and Artificial Intelligence in Spine: Current and Future Perspectives

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