Geissinger et al., 2020 - Google Patents
Motion inference using sparse inertial sensors, self-supervised learning, and a new dataset of unscripted human motionGeissinger et al., 2020
View HTML- Document ID
- 2203213194771902446
- Author
- Geissinger J
- Asbeck A
- Publication year
- Publication venue
- Sensors
External Links
Snippet
In recent years, wearable sensors have become common, with possible applications in biomechanical monitoring, sports and fitness training, rehabilitation, assistive devices, or human-computer interaction. Our goal was to achieve accurate kinematics estimates using a …
- 238000010801 machine learning 0 abstract description 14
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F19/00—Digital computing or data processing equipment or methods, specially adapted for specific applications
- G06F19/30—Medical informatics, i.e. computer-based analysis or dissemination of patient or disease data
- G06F19/34—Computer-assisted medical diagnosis or treatment, e.g. computerised prescription or delivery of medication or diets, computerised local control of medical devices, medical expert systems or telemedicine
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F19/00—Digital computing or data processing equipment or methods, specially adapted for specific applications
- G06F19/30—Medical informatics, i.e. computer-based analysis or dissemination of patient or disease data
- G06F19/32—Medical data management, e.g. systems or protocols for archival or communication of medical images, computerised patient records or computerised general medical references
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06Q—DATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—Systems or methods specially adapted for a specific business sector, e.g. utilities or tourism
- G06Q50/10—Services
- G06Q50/20—Education
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06Q—DATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—Systems or methods specially adapted for a specific business sector, e.g. utilities or tourism
- G06Q50/10—Services
- G06Q50/22—Health care, e.g. hospitals; Social work
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06Q—DATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/10—Office automation, e.g. computer aided management of electronic mail or groupware; Time management, e.g. calendars, reminders, meetings or time accounting
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06Q—DATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—Systems or methods specially adapted for a specific business sector, e.g. utilities or tourism
- G06Q50/01—Social networking
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
- G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N99/00—Subject matter not provided for in other groups of this subclass
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F3/00—Input arrangements for transferring data to be processed into a form capable of being handled by the computer; Output arrangements for transferring data from processing unit to output unit, e.g. interface arrangements
- G06F3/01—Input arrangements or combined input and output arrangements for interaction between user and computer
-
- G—PHYSICS
- G09—EDUCATION; CRYPTOGRAPHY; DISPLAY; ADVERTISING; SEALS
- G09B—EDUCATIONAL OR DEMONSTRATION APPLIANCES; APPLIANCES FOR TEACHING, OR COMMUNICATING WITH, THE BLIND, DEAF OR MUTE; MODELS; PLANETARIA; GLOBES; MAPS; DIAGRAMS
- G09B19/00—Teaching not covered by other main groups of this subclass
Similar Documents
Publication | Publication Date | Title |
---|---|---|
Logacjov et al. | HARTH: a human activity recognition dataset for machine learning | |
Fridriksdottir et al. | Accelerometer-based human activity recognition for patient monitoring using a deep neural network | |
Wouda et al. | Estimation of full-body poses using only five inertial sensors: an eager or lazy learning approach? | |
Hachaj et al. | Human actions analysis: Templates generation, matching and visualization applied to motion capture of highly-skilled karate athletes | |
Reining et al. | Human activity recognition for production and logistics—a systematic literature review | |
Geissinger et al. | Motion inference using sparse inertial sensors, self-supervised learning, and a new dataset of unscripted human motion | |
Wu et al. | Yoga posture recognition and quantitative evaluation with wearable sensors based on two-stage classifier and prior Bayesian network | |
Vonstad et al. | Comparison of a deep learning-based pose estimation system to marker-based and kinect systems in exergaming for balance training | |
Liu et al. | Canoeing motion tracking and analysis via multi-sensors fusion | |
Echeverria et al. | Toward modeling psychomotor performance in karate combats using computer vision pose estimation | |
Chatzitofis et al. | DeepMoCap: Deep optical motion capture using multiple depth sensors and retro-reflectors | |
Arrowsmith et al. | Physiotherapy exercise classification with single-camera pose detection and machine learning | |
Kim et al. | Fusion poser: 3D human pose estimation using sparse IMUs and head trackers in real time | |
Fortes Rey et al. | Translating videos into synthetic training data for wearable sensor-based activity recognition systems using residual deep convolutional networks | |
Haralabidis et al. | Fusing accelerometry with videography to monitor the effect of fatigue on punching performance in elite boxers | |
Ruffaldi et al. | Sensor fusion for complex articulated body tracking applied in rowing | |
Balasubramanyam et al. | Motion-sphere: Visual representation of the subtle motion of human joints | |
Pandurevic et al. | Analysis of competition and training videos of speed climbing athletes using feature and human body keypoint detection algorithms | |
Fong et al. | Training classifiers with shadow features for sensor-based human activity recognition | |
Wouda et al. | Time coherent full-body poses estimated using only five inertial sensors: Deep versus shallow learning | |
Chatzis et al. | Automatic ergonomic risk assessment using a variational deep network architecture | |
Oña et al. | Automatic outcome in manual dexterity assessment using colour segmentation and nearest neighbour classifier | |
Sarker et al. | Capturing upper body kinematics and localization with low-cost sensors for rehabilitation applications | |
Wu et al. | Development of ai algorithm for weight training using inertial measurement units | |
Konak et al. | IMU-based movement trajectory heatmaps for human activity recognition |