Filtern
Dokumenttyp
Sprache
- Englisch (7)
Volltext vorhanden
- ja (7)
Gehört zur Bibliographie
- nein (7)
Schlagworte
- Elektrode (2)
- Flüssigkristalline Polymere (2)
- Parkinson-Krankheit (2)
- electrode model (2)
- Astrozyt (1)
- Bewegung (1)
- Bewegungsstörung (1)
- Depression (1)
- Elektrischer Leiter (1)
- Elektrokortikogramm (1)
- Elektrophysiologie (1)
- Essenzieller Tremor (1)
- Ganganalyse (1)
- Gehen (1)
- Hirnstimulation (1)
- Konfokale Mikroskopie (1)
- Kupfer (1)
- Laser-Rastermikroskopie (1)
- Latenzzeit <Informatik> (1)
- Magnetische Stimulation (1)
- Maschinelles Sehen (1)
- Messung (1)
- Nervenkrankheit (1)
- OLED (1)
- OLED latency (1)
- Pedografie (1)
- Psychometrie (1)
- Rauigkeit (1)
- Reaktionszeit (1)
- Sensor (1)
- Stereokamera (1)
- Transkranielle magnetische Stimulation (1)
- Zentralnervensystem (1)
- astrocytes (1)
- charge storage capacity (CSCc) (1)
- cognitive performance assessment (1)
- computer vision (1)
- confocal microscopy (1)
- copper conductors (1)
- cortical electrical stimulation (1)
- cortical stimulation (1)
- deep brain stimulation (1)
- electrocorticogram (1)
- electrode impedance (1)
- electroplating (1)
- foot pressure sensors (1)
- human gait (1)
- in vivo two-photon laser-scanning microscopy (1)
- liquid crystal polymer electrodes (1)
- material extrusion (1)
- movement disorders (1)
- neuron-glia interaction (1)
- output impedance (1)
- platinum (1)
- pose estimation (1)
- pulsed current (1)
- reaction time measurement (1)
- risk of falls (1)
- spectral estimation (1)
- stereoscopic cameras (1)
- stimulator characterization (1)
- stimulator model (1)
- surface roughness (1)
- tremor (1)
- ultrasound (1)
- wearable motion sensors (1)
Institut
- FB Technik (7)
Gait analysis is a systematic study of human movement. Combining wearable foot pressure sensors and machine learning (ML) solutions for a high-fidelity body pose tracking from RGB video frames could reveal more insights into gait abnormalities. However, accurate detection of heel strike (HS) and toe-off (TO) events is crucial to compute interpretable gait parameters. In this work, we present an experimental platform to study the timing of gait events using a new wearable foot pressure sensor (ActiSense System, IEE S.A., Luxembourg), and Google’s open-source ML solution MediaPipe Pose. For this purpose, two StereoPi systems were built to capture stereoscopic videos and images in real time. MediaPipe Pose was applied to the synchronized StereoPi cameras, and two algorithms (ALs) were developed to detect HS and TO events for gait and analysis. Preliminary results from a healthy subject walking on a treadmill show a mean relative deviation across all time spans of less than 4% for the ActiSense device and less than 16% for AL2 (33% for AL1) employing MediaPipe Pose on StereoPi videos. Finally, this work offers a platform for the development of sensor- and video-based ALs to automatically identify the timing of gait events in healthy individuals and those with gait disorders.