Smart Insole: A Gait Analysis Monitoring Platform Targeting Parkinson Disease Patients Based on Insoles

Boucharas, Dimitrios G.; Androutsos, Christos; Gkois, George; Tsakanikas, Vassilis; Pezoulas, Vasileios C.; Manousos, Dimitrios; Skaramagkas, Vasileios; Chatzaki, Chariklia; Kontogiannis, Stathis; Spandonidis, Christos; Pantazis, Alexandros; Tachos, Nikolaos S.; Tsiknakis, Manolis; Fotiadis, Dimitrios I.

doi:10.48550/arxiv.2212.00109

Cited by 2 publications

(3 citation statements)

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“…More recent studies have expanded the application of plantar pressure analysis using smart insoles to include disease diagnosis. For instance, one study demonstrated the potential for the early detection of Parkinson’s disease using smart insoles, whereas another study suggested the use of smart insoles to predict and classify sarcopenia [ 17 , 35 ]. Ongoing efforts also aim to utilize smart insoles for treatment purposes, involving monitoring plantar pressure and inducing alterations in its distribution [ 36 ].…”

Section: Discussionmentioning

confidence: 99%

“…Since the inception of smart insoles, numerous studies have explored their applications in the medical field. These studies encompass a wide range of areas, including but not limited to gait and posture stability analysis, the prevention of foot wounds in individuals with diabetes, and the assessment of fall risk in older adults by analyzing the distribution of plantar pressure [ 14 , 15 , 16 , 17 , 18 , 19 ]. Notably, several studies have reported high accuracies for gait and postural analyses using real-time plantar pressure monitoring, leading to a gradual expansion of applications for smart insoles [ 20 ].…”

Section: Introductionmentioning

confidence: 99%

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Validation of the Short Physical Performance Battery via Plantar Pressure Analysis Using Commercial Smart Insoles

Jang,

Park,

Paek

et al. 2023

Sensors

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This cross-sectional study, conducted at a tertiary care hospital’s rehabilitation clinic, aimed to validate Short Physical Performance Battery (SPPB) results obtained through plantar pressure analysis using commercial smart insoles (SPPB-SI) and to compare these results to manually acquired results by an experienced examiner (SPPB-M). This study included 40 independent-walking inpatients and outpatients aged 50 or older. SPPB-SI and SPPB-M were administered concurrently, with the smart insoles providing plantar pressure data that were converted into time–pressure curves. Two interpreters assessed the curves, determining component completion times for the SPPB-SI scores. Among the 40 participants (mean age: 72.98, SD: 9.27), the mean total SPPB-SI score was 7.72 ± 2.50, and the mean total SPPB-M score was 7.95 ± 2.63. The time recordings and measured scores of each SPPB-SI component exhibited high reliability with inter- and intra-interpreter correlation coefficients of 0.9 and 0.8 or higher, respectively. The intraclass correlation coefficient between the total SPPB-SI and SPPB-M scores was 0.831 (p < 0.001), and that between the component scores of the two measurements ranged from 0.837 to 0.901 (p < 0.001). Consistent correlations with geriatric functional parameters were observed for both SPPB-SI and SPPB-M. This study underscores the potential of commercial smart insoles as reliable tools for conducting SPPB assessments.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Validation of the Short Physical Performance Battery via Plantar Pressure Analysis Using Commercial Smart Insoles

Jang,

Park,

Paek

et al. 2023

Sensors

View full text Add to dashboard Cite

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“…The detection of typical gait events is a helpful technique for improving gait analysis, evaluating therapies for abnormal gait, and developing devices and sensors for gait support [22], [107], [108]. Therefore, gait analysis requires the quantified study of the parameters related to force, time and distance, by calculating a range of important temporal and spatial characteristics [109].…”

Section: Sensory Equipment For Detection Of Pd-related Clinical Signsmentioning

confidence: 99%

Multi-Modal Deep Learning Diagnosis of Parkinson’s Disease—A Systematic Review

Skaramagkas

Pentari

Kefalopoulou

et al. 2023

IEEE Trans. Neural Syst. Rehabil. Eng.

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Parkinson's Disease (PD) is among the most frequent neurological disorders. Approaches that employ artificial intelligence and notably deep learning, have been extensively embraced with promising outcomes. This study dispenses an exhaustive review between 2016 and January 2023 on deep learning techniques used in the prognosis and evolution of symptoms and characteristics of the disease based on gait, upper limb movement, speech and facial expression-related information as well as the fusion of more than one of the aforementioned modalities. The search resulted in the selection of 87 original research publications, of which we have summarized the relevant information regarding the utilized learning and development process, demographic information, primary outcomes, and sensory equipment related information. Various deep learning algorithms and frameworks have attained state-of-theart performance in many PD-related tasks by outperforming conventional machine learning approaches, according to the research reviewed. In the meanwhile, we identify significant drawbacks in the existing research, including a lack of data availability and interpretability of models. The fast advancements in deep learning and the rise in accessible data provide the opportunity to address these difficulties in the near future and for the broad application of this technology in clinical settings.

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Smart Insole: A Gait Analysis Monitoring Platform Targeting Parkinson Disease Patients Based on Insoles

Cited by 2 publications

References 0 publications

Validation of the Short Physical Performance Battery via Plantar Pressure Analysis Using Commercial Smart Insoles

Validation of the Short Physical Performance Battery via Plantar Pressure Analysis Using Commercial Smart Insoles

Multi-Modal Deep Learning Diagnosis of Parkinson’s Disease—A Systematic Review

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