Real-Time Monitoring of Charge Accumulation from Insole-Mounted MFC Piezoelectric Modules for Analysis of Power Availability in Mobility-Restricted Patients
Notice bibliographique
Résumé
Almost 10 million people suffer from musculoskeletal conditions in the UK alone, causing pain and reduced mobility, with knock-on impact to medical consultations and associated cost, ability to work, mental health and reduced quality of life.Collection of biomechanical data between clinics would allow optimisation of treatment, improving patient outcomes and pain scores.Advances in telemedicine and miniaturised, low-power wireless wearables could be exploited to better understand patient mobility away from the clinic, and inform treatment regimes.A self-powered active monitoring shoe insole would enable such data to be collected, and remotely monitored and analysed, with no intervention from the patient.Monitoring activity at the foot both affords a level of human-generated power not available elsewhere upon the body [1], and an ideal location for monitoring pressure distribution, joint off-loading and temporal gait characteristics.Piezoelectric Macro Fiber Composite (MFC) materials provide a low-profile and flexible form-factor ideal for embedding into shoe insoles to exploit power generated during walking [2,3].Likewise, flat and flexible force-sensing resistive sensors, combined with miniaturised MEMS accelerometers can provide data with proven application to gait analysis [4].A challenge to power generation in musculoskeletal patients however is that mobility-restricted users would produce inherently lower levels of power due to reduced levels of activity.The experimentation reported in this paper determines first a baseline of power generation for different rates of walking for MFC modules placed at the heel and forefoot within the insole in able-bodied participants.Accumulation of charge for different activities of daily living expected of those with motion-restricting musculoskeletal conditions will then be presented, collected from a cohort of participants noting their height and weight.Such activities include: walking at a comfortable speed over short distances, walking up and down short flights of stairs, sitting to standing, standing to sitting, getting in and out of bed, and other typical activities.Experimental set up comprises an insole fitted with two MFC modules measuring 85x57mm in the forefoot and 56x28mm in the heel of the insole, with charge accumulation data collected wirelessly using a Bluetooth-enabled wireless sensor node to a laptop.The wireless node additionally monitors accelerometer data for accurate comparison of charge generation against different rates of walking and to better understand the activities' charge-generating capabilities.Additionally motion capture and accelerometer data (collected using gold standard facilities at Cardiff's Musculoskeletal Biomechanics Research Facility (MSKBRF) which includes motion capture, force plates and instrumented treadmill) will also be used to determine a lower boundary for sampling rate of sensors to yield useful biomechanical data of the activities of interest.State of the art ultra-low power microcontrollers and sensing circuitry will be compared to present ranges of power consumption for different levels of data collection activity.This will allow proposals for optimum sampling rate in order to yield appropriate activity data without exceeding available power levels, and methods for adapting both number of active sensors and their sampling rate based upon current level of patient activity, to ensure energy demand does not exceed supply.A proposal for power-optimal upload of data for Telehealth monitoring in a practical home scenario will also be presented.
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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».