Monitoring of Posture Allocations and Activities by a Shoe-Based Wearable Sensor

• Published in: IEEE transactions on biomedical engineering Vol. 58; no. 4; pp. 983 - 990
• Main Authors: Sazonov, Edward S., Fulk, George, Hill, James, Schutz, Yves, Browning, Raymond
• Format: Journal Article
• Language: English
• Published: New York, NY IEEE 01.04.2011; Institute of Electrical and Electronics Engineers; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Acceleration; Adolescent; Adult; Applied sciences; Biological and medical sciences; Biomedical monitoring; energy expenditure; Equipment Design; Equipment Failure Analysis; Exact sciences and technology; Feature extraction; Female; Foot - physiology; Footwear; Fundamental and applied biological sciences. Psychology; Humans; Information, signal and communications theory; Legged locomotion; Male; Manometry - instrumentation; Medical sciences; Metabolic diseases; Monitoring; Monitoring, Ambulatory - instrumentation; Obesity; Pattern recognition; Performance evaluation; Posture; Posture - physiology; posture and activity recognition; Reproducibility of Results; Sensitivity and Specificity; Sensor phenomena and characterization; Shoes; Signal processing; Support vector machines; Telecommunications and information theory; Testing; Transducers; Vertebrates: body movement. Posture. Locomotion. Flight. Swimming. Physical exercise. Rest. Sports; wearable devices; Wearable sensors; Weight-Bearing - physiology; Young Adult; Obesity; wearable devices; Nutrition disorder; Measurement sensor; Support vector machine; Pattern recognition; Shoe; energy expenditure; Posture; posture and activity recognition; Surveillance; Energetic cost; Portable equipment; Biomedical monitoring; Nutritional status; Monitoring; Biomedical engineering
• ISSN: 0018-9294; 1558-2531; 1558-2531
• DOI: 10.1109/TBME.2010.2046738

Monitoring of posture allocations and activities enables accurate estimation of energy expenditure and may aid in obesity prevention and treatment. At present, accurate devices rely on multiple sensors distributed on the body and thus may be too obtrusive for everyday use. This paper presents a novel wearable sensor, which is capable of very accurate recognition of common postures and activities. The patterns of heel acceleration and plantar pressure uniquely characterize postures and typical activities while requiring minimal preprocessing and no feature extraction. The shoe sensor was tested in nine adults performing sitting and standing postures and while walking, running, stair ascent/descent and cycling. Support vector machines (SVMs) were used for classification. A fourfold validation of a six-class subject-independent group model showed 95.2% average accuracy of posture/activity classification on full sensor set and over 98% on optimized sensor set. Using a combination of acceleration/pressure also enabled a pronounced reduction of the sampling frequency (25 to 1 Hz) with out significant loss of accuracy (98% versus 93%). Subjects had shoe sizes (US) M9.5-11 and W7-9 and body mass index from 18.1 to 39.4 kg/m2 and thus suggesting that the device can be used by individuals with varying anthropometric characteristics.