Ambulatory monitoring of human posture and walking speed using wearable accelerometer sensors

Yeoh, Wee-Soon; Pek, Isaac; Yong, Yi-Han; Chen, Xiang; Waluyo, Agustinus Borgy

doi:10.1109/iembs.2008.4650382

Cited by 51 publications

(34 citation statements)

References 3 publications

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“…It is interesting to note that the average speed variation between normal gait and mild knee condition gait was only 0.07 m/s. Such a difference cannot be easily detected relying only on a single accelerometer [37].…”

Section: A Controlled Experimentsmentioning

confidence: 99%

An On-Node Processing Approach for Anomaly Detection in Gait

et al. 2015

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Abstract-A novel method is proposed for capturing deviation in gait using a wearable accelerometer. Previous research has outlined the importance of gait analysis to assess frailty and fall risk in elderly patients. Several solutions, based on wearable sensors, have been proposed to assist geriatricians in mobility assessment tests, such as the Timed Up-and-Go test. However, these methods can be applied only to supervised scenarios and do not allow continuous and unobtrusive monitoring of gait. The method we propose is designed to achieve continuous monitoring of gait in a completely unsupervised fashion, requiring the use of a single waist-mounted accelerometer. The user's gait patterns are automatically learned using specific acceleration-based features, while anomaly detection is used to capture subtle changes in the way the user walks. All the required processing can be executed in real-time on the wearable device. The method was evaluated with 30 volunteers, who simulated a knee flexion impairment. On average, our method obtained ∼ 84% accuracy in the recognition of abnormal gait segments lasting ∼ 5 s. Prompt detection of gait anomalies could enable early intervention and prevent falls.

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Section: A Controlled Experimentsmentioning

confidence: 99%

An On-Node Processing Approach for Anomaly Detection in Gait

et al. 2015

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“…On the other hand, gravity-only acceleration measurements could be useful for some health care applications. Posture or risk of falling could be determined while a person is in motion [9].…”

Section: Introductionmentioning

confidence: 99%

Motion Based Acceleration Correction for Improved Sensor Orientation Estimates

Bennett

Jafari

Gans

2014

2014 11th International Conference on Wearable and Implantable Body Sensor Networks

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Inertial measurement units (IMUs) including accelerometers and gyroscopes are becoming very common and can be found in cell phones, fitness trackers, and other wearable devices. With the growth in wearable computing and body sensor networks, IMUs are also becoming more prevalent research environments for estimation and tracking of human motion. We demonstrate that the accelerometer angle estimate is inaccurate for typical motions and present a method using a kinematic model to correct the accelerometer angle estimate and improve overall orientation estimates using an extended Kalman filter (EKF). Our method improves upon the raw accelerometer orientation estimation method and a gyroscope based EKF method for sensor orientation during motion and at rest.

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“…Chest [25,[27][28][29][30][31][32][33] Thigh [10,19,28,30,34] Ankle [24,25,27] Wrist [10,23,24,27,30,33,34] Waist [10,[19][20][21][22][23][24][25][26][27] Leg [34] Head [35,36] Upper arm [10,35] In several studies [37][38][39], multiple sensors have been used either to improve detection accuracy and/or to find an optimal placement. Although higher classification accuracy has been reported with the use of multiple sensors [38], multi-sensor fusion will introduce several research challenges as discussed in [40].…”

Section: Introductionmentioning

confidence: 99%

Analysis of Optimal Sensor Positions for Activity Classification and Application on a Different Data Collection Scenario

Pannurat¹,

Thiemjarus²,

Nantajeewarawat³

et al. 2017

Preprint

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This paper focuses on optimal sensor positioning for monitoring activities of daily living and investigates different combinations of features and models on different sensor positions, i.e., the side of the waist, front of the waist, chest, thigh, head, upper arm, wrist, and ankle. Nineteen features are extracted and the feature importance is measured by using the Relief-F feature selection algorithm. Eight classification algorithms are evaluated on a dataset collected from young subjects and that collected from elderly subjects, with two different experimental settings. To deal with different sampling rates, signals with a high data rate are down-sampled and a transformation matrix is used for aligning signals to the same coordinate system. The thigh, chest, side of the waist, and front of the waist are the best four sensor positions for the first dataset (young subjects), with average accuracy values being greater than 96%. The best model obtained from the first dataset for the side of the waist is validated on the second dataset (elderly subjects). The most appropriate number of features for each sensor position is reported. The results provide a reference for building activity recognition models for different sensor positions, as well as for data acquired from different hardware platforms and subject groups.

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Ambulatory monitoring of human posture and walking speed using wearable accelerometer sensors

Cited by 51 publications

References 3 publications

An On-Node Processing Approach for Anomaly Detection in Gait

An On-Node Processing Approach for Anomaly Detection in Gait

Motion Based Acceleration Correction for Improved Sensor Orientation Estimates

Analysis of Optimal Sensor Positions for Activity Classification and Application on a Different Data Collection Scenario

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