Asynchronous Averaging of Gait Cycles for Classification of Gait and Device Modes

Autor: Gustaf Hendeby, Fredrik Gustafsson, Parinaz Kasebzadeh
Jazyk: angličtina
Rok vydání: 2021
Předmět:
Signal Processing (eess.SP)
FOS: Computer and information sciences
Computer Science - Machine Learning
Inertial frame of reference
Computer science
Optimization
Sensors
Smart phones
Measurement units
Fourier series
Legged locomotion
Feature extraction
Pedestrian dead reckoning
gait cycles
inertial measurement unit (IMU)
ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION
01 natural sciences
Machine Learning (cs.LG)
Gait (human)
Inertial measurement unit
FOS: Electrical engineering
electronic engineering
information engineering
Annan elektroteknik och elektronik
Electrical and Electronic Engineering
Electrical Engineering and Systems Science - Signal Processing
Instrumentation
Other Electrical Engineering
Electronic Engineering
Information Engineering
business.industry
010401 analytical chemistry
Pattern recognition
Gait
Signature (logic)
0104 chemical sciences
ComputingMethodologies_PATTERNRECOGNITION
Artificial intelligence
business
Popis: An approach for computing unique gait signature using measurements collected from body-worn inertial measurement units (IMUs) is proposed. The gait signature represents one full cycle of the human gait, and is suitable for off-line or on-line classification of the gait mode. The signature can also be used to jointly classify the gait mode and the device mode. The device mode identifies how the IMU-equipped device is being carried by the user. The method is based on precise segmentation and resampling of the measured IMU signal, as an initial step, further tuned by minimizing the variability of the obtained signature within each gait cycle. Finally, a Fourier series expansion of the gait signature is introduced which provides a low-dimensional feature vector well suited for classification purposes. The proposed method is evaluated on a large dataset involving several subjects, each one containing two different gait modes and four different device modes. The gait signatures enable a high classification rate for each step cycle.
Submitted to IEEE Sensors Journal
Databáze: OpenAIRE
Externí odkaz: