Estimating wearable motion sensor performance from personal biomechanical models and sensor data synthesis
Abstract We present a fundamentally new approach to design and assess wearable motion systems based on biomechanical simulation and sensor data synthesis. We devise a methodology of personal biomechanical models and virtually attach sensor models to body parts, including sensor positions frequently...
Guardado en:
Autores principales: | Adrian Derungs, Oliver Amft |
---|---|
Formato: | article |
Lenguaje: | EN |
Publicado: |
Nature Portfolio
2020
|
Materias: | |
Acceso en línea: | https://doaj.org/article/ca5c4f25e12b463190f470f24354f167 |
Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
Ejemplares similares
-
Wearable motion sensors and digital biomarkers in stroke rehabilitation
por: Derungs Adrian, et al.
Publicado: (2020) -
A high performance wearable strain sensor with advanced thermal management for motion monitoring
por: Cenxiao Tan, et al.
Publicado: (2020) -
Dyskinesia estimation during activities of daily living using wearable motion sensors and deep recurrent networks
por: Murtadha D. Hssayeni, et al.
Publicado: (2021) -
Wearable multifunctional printed graphene sensors
por: Altynay Kaidarova, et al.
Publicado: (2019) -
Deep learning based human activity recognition (HAR) using wearable sensor data
por: Saurabh Gupta
Publicado: (2021)