Weightlessness feature &amp;#x2014; a novel feature for single tri-axial accelerometer based activity recognition

He, Zhenyu; Liu, Zhibin; Jin, Lianwen; Zhen, Li-Xin; Huang, Jiancheng

doi:10.1109/icpr.2008.4761688

Cited by 9 publications

(2 citation statements)

References 9 publications

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“…In the calibration phase, the resulting acceleration (r), the proportion of acceleration in the absolute vertical axis (v) and the proportion of the acceleration in the horizontal plane (h) are calculated from the original acceleration vector a. These calibrated signals (r, v, h) are more independent of the actual orientation of the mounted sensor (He et al 2008).…”

Section: Acceleration Featuresmentioning

confidence: 99%

A stepwise validation of a wearable system for estimating energy expenditure in field-based research

Rumo¹,

Amft²,

Tröster³

et al. 2011

Physiol. Meas.

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Regular physical activity (PA) is an important contributor to a healthy lifestyle. Currently, standard sensor-based methods to assess PA in field-based research rely on a single accelerometer mounted near the body's center of mass. This paper introduces a wearable system that estimates energy expenditure (EE) based on seven recognized activity types. The system was developed with data from 32 healthy subjects and consists of a chest mounted heart rate belt and two accelerometers attached to a thigh and dominant upper arm. The system was validated with 12 other subjects under restricted lab conditions and simulated free-living conditions against indirect calorimetry, as well as in subjects' habitual environments for 2 weeks against the doubly labeled water method. Our stepwise validation methodology gradually trades reference information from the lab against realistic data from the field. The average accuracy for EE estimation was 88% for restricted lab conditions, 55% for simulated free-living conditions and 87% and 91% for the estimation of average daily EE over the period of 1 and 2 weeks.

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Section: Acceleration Featuresmentioning

confidence: 99%

A stepwise validation of a wearable system for estimating energy expenditure in field-based research

Rumo¹,

Amft²,

Tröster³

et al. 2011

Physiol. Meas.

View full text Add to dashboard Cite

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“…Furthermore, analyzing the three-axis acceleration data before and after filtering revealed that the acceleration data from individual axes did not sufficiently capture the characteristics of sheep walking behavior, relying solely on this data was inadequate for accurately determining the number of steps. As a result, this paper adopted the approach of using combined acceleration to accurately calculate the final step count [16][17][18]. The formula for calculating the combined acceleration is presented in Equation (1).…”

Section: Data Processingmentioning

confidence: 99%

Research on Six-Axis Sensor-Based Step-Counting Algorithm for Grazing Sheep

Jiang

et al. 2023

Sensors

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Step counting is an effective method to assess the activity level of grazing sheep. However, existing step-counting algorithms have limited adaptability to sheep walking patterns and fail to eliminate false step counts caused by abnormal behaviors. Therefore, this study proposed a step-counting algorithm based on behavior classification designed explicitly for grazing sheep. The algorithm utilized regional peak detection and peak-to-valley difference detection to identify running and leg-shaking behaviors in sheep. It distinguished leg shaking from brisk walking behaviors through variance feature analysis. Based on the recognition results, different step-counting strategies were employed. When running behavior was detected, the algorithm divided the sampling window by the baseline step frequency and multiplied it by a scaling factor to accurately calculate the number of steps for running. No step counting was performed for leg-shaking behavior. For other behaviors, such as slow and brisk walking, a window peak detection algorithm was used for step counting. Experimental results demonstrate a significant improvement in the accuracy of the proposed algorithm compared to the peak detection-based method. In addition, the experimental results demonstrated that the average calculation error of the proposed algorithm in this study was 6.244%, while the average error of the peak detection-based step-counting algorithm was 17.556%. This indicates a significant improvement in the accuracy of the proposed algorithm compared to the peak detection method.

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A Wearable Accelerometer System for Unobtrusive Monitoring of Parkinson's Diease Motor Symptoms

Khan

Barnathan

Montgomery

et al. 2014

2014 IEEE International Conference on Bioinformatics and Bioengineering

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Parkinson's disease is a complex condition currently monitored at home with paper diaries which rely on subjective and unreliable assessment of motor function at nonstandard time intervals. We present an innovative wearable and unobtrusive monitoring system for patients which can help provide physicians with significantly improved assessment of patients' responses to drug therapies and lead to better-targeted treatment regimens. In this paper we describe the algorithmic development of the system and an evaluation in patients for assessing the onset and duration of advanced PD motor symptoms.

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Weightlessness feature — a novel feature for single tri-axial accelerometer based activity recognition

Cited by 9 publications

References 9 publications

A stepwise validation of a wearable system for estimating energy expenditure in field-based research

A stepwise validation of a wearable system for estimating energy expenditure in field-based research

Research on Six-Axis Sensor-Based Step-Counting Algorithm for Grazing Sheep

A Wearable Accelerometer System for Unobtrusive Monitoring of Parkinson's Diease Motor Symptoms

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