Proof of Concept in Artificial-Intelligence-Based Wearable Gait Monitoring for Parkinson’s Disease Management Optimization

Ileșan, Robert Radu; Cordoș, Claudia-Georgiana; Mihăilă, Laura-Ioana; Fleșar, Radu; Popescu, Ana-Sorina; Perju‐Dumbravă, Lăcrămioara; Faragó, Paul

doi:10.3390/bios12040189

Cited by 20 publications

(15 citation statements)

References 77 publications

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“…Data collected on the gait of PD subjects with wearable accelerometers is suitable for Artificial Intelligence (AI) or IoT decisional support [ 36 , 41 ]. AI-based wearable gait monitoring is already used for optimization of Parkinson’s disease management [ 41 ]. We figured that smartphone applications based on AI can be applied to monitor gait characteristics in PD subjects.…”

Section: Discussionmentioning

confidence: 99%

Gait Characteristics Analyzed with Smartphone IMU Sensors in Subjects with Parkinsonism under the Conditions of “Dry” Immersion

Meigal

Gerasimova-Meigal

Reginya

et al. 2022

Sensors

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Parkinson’s disease (PD) is increasingly being studied using science-intensive methods due to economic, medical, rehabilitation and social reasons. Wearable sensors and Internet of Things-enabled technologies look promising for monitoring motor activity and gait in PD patients. In this study, we sought to evaluate gait characteristics by analyzing the accelerometer signal received from a smartphone attached to the head during an extended TUG test, before and after single and repeated sessions of terrestrial microgravity modeled with the condition of “dry” immersion (DI) in five subjects with PD. The accelerometer signal from IMU during walking phases of the TUG test allowed for the recognition and characterization of up to 35 steps. In some patients with PD, unusually long steps have been identified, which could potentially have diagnostic value. It was found that after one DI session, stepping did not change, though in one subject it significantly improved (cadence, heel strike and step length). After a course of DI sessions, some characteristics of the TUG test improved significantly. In conclusion, the use of accelerometer signals received from a smartphone IMU looks promising for the creation of an IoT-enabled system to monitor gait in subjects with PD.

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Section: Discussionmentioning

confidence: 99%

Gait Characteristics Analyzed with Smartphone IMU Sensors in Subjects with Parkinsonism under the Conditions of “Dry” Immersion

Meigal

Gerasimova-Meigal

Reginya

et al. 2022

Sensors

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“…Recent (AI)-based wearables are helping with early diagnosis, diagnostic accuracy, long-term monitoring of Parkinson's' disease (PD), and better management of therapeutic strategies. Ilesan et al [226] offered a wearable gait assessment system with pressure sensors on a physiograph and convolutional neural network (CNN). This enabled bilateral tracking of the foot biomechanics by means of plantar pressure distribution and lower-limb EMG in correlation with upper limb balance.…”

Section: Gait Analysis and Fall Detectionmentioning

confidence: 99%

The Emergence of AI-Based Wearable Sensors for Digital Health Technology: A Review

Shajari,

Kuruvinashetti,

Komeili

et al. 2023

Sensors

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Disease diagnosis and monitoring using conventional healthcare services is typically expensive and has limited accuracy. Wearable health technology based on flexible electronics has gained tremendous attention in recent years for monitoring patient health owing to attractive features, such as lower medical costs, quick access to patient health data, ability to operate and transmit data in harsh environments, storage at room temperature, non-invasive implementation, mass scaling, etc. This technology provides an opportunity for disease pre-diagnosis and immediate therapy. Wearable sensors have opened a new area of personalized health monitoring by accurately measuring physical states and biochemical signals. Despite the progress to date in the development of wearable sensors, there are still several limitations in the accuracy of the data collected, precise disease diagnosis, and early treatment. This necessitates advances in applied materials and structures and using artificial intelligence (AI)-enabled wearable sensors to extract target signals for accurate clinical decision-making and efficient medical care. In this paper, we review two significant aspects of smart wearable sensors. First, we offer an overview of the most recent progress in improving wearable sensor performance for physical, chemical, and biosensors, focusing on materials, structural configurations, and transduction mechanisms. Next, we review the use of AI technology in combination with wearable technology for big data processing, self-learning, power-efficiency, real-time data acquisition and processing, and personalized health for an intelligent sensing platform. Finally, we present the challenges and future opportunities associated with smart wearable sensors.

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“…Regardless of the age of disease onset and level of disease care, the average age at the death is similar (mid-70s) [36] . [16] .…”

Section: Diagnosis Of Parkinson's With the Help Of Aimentioning

confidence: 99%

Harnessing artificial intelligence for enhanced Parkinson’s disease management: Pathways, treatment, and prospects

Patwekar,

Sanaullah

et al. 2023

Trends Immunother.

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Parkinson’s disease (PD) is a neurodegenerative disorder characterized by the accumulation of misfolded proteins and impaired protein degradation mechanisms. Dysregulation of protein degradation processes, including autophagy and the ubiquitin-proteasome system, has been implicated in the pathogenesis of PD. Recently, artificial intelligence (AI) has emerged as a powerful tool to enhance our understanding of protein degradation in PD. This abstract provides an overview of the advancements in studying protein degradation in PD with the aid of AI. The integration of AI techniques, such as machine learning and data mining, has enabled the identification and characterization of protein degradation pathways involved in PD. By analyzing large-scale protein-protein interaction networks, AI algorithms have revealed key interactions and pathways underlying protein degradation dysfunction in PD. Furthermore, AI models can predict the efficiency of protein degradation processes and identify potential targets for enhancing protein degradation in PD, aiding in the development of novel therapeutic interventions.AI-based approaches have also been instrumental in drug discovery and target identification, as they can screen vast databases of compounds to identify potential drugs or small molecules that modulate protein degradation pathways relevant to PD. Additionally, deep learning algorithms have facilitated the analysis of protein structures, predicting protein stability and folding patterns that impact protein degradation. Moreover, AI has played a crucial role in the identification of protein biomarkers associated with protein degradation dysfunction in PD. These biomarkers can aid in early diagnosis and monitoring of the disease, enabling timely intervention and personalized treatment strategies. The advancements presented in this abstract highlight the transformative potential of AI in elucidating the intricate mechanisms of protein degradation in PD. Collaborations between AI researchers, biologists, and clinicians are essential to translate these findings into effective diagnostic tools and therapeutic interventions for PD patients.

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Proof of Concept in Artificial-Intelligence-Based Wearable Gait Monitoring for Parkinson’s Disease Management Optimization

Cited by 20 publications

References 77 publications

Gait Characteristics Analyzed with Smartphone IMU Sensors in Subjects with Parkinsonism under the Conditions of “Dry” Immersion

Gait Characteristics Analyzed with Smartphone IMU Sensors in Subjects with Parkinsonism under the Conditions of “Dry” Immersion

The Emergence of AI-Based Wearable Sensors for Digital Health Technology: A Review

Harnessing artificial intelligence for enhanced Parkinson’s disease management: Pathways, treatment, and prospects

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