Sensor Positioning for Activity Recognition Using Wearable Accelerometers

By L. Atallah, Benny P. L. Lo, R. King, Guang-Zhong Yang. IEEE Transactions on Biomedical Circuits and Systems, 2011

340 citations recorded by the source corpus.

Subfields: Biomedical Engineering, Computer Vision and Pattern Recognition, Physiology

View Paper: https://www.semanticscholar.org/paper/ca2e6836393f35026479daeccda7e83456438146

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Biomedical Engineering

In Computer Vision and Pattern Recognition

In Physiology

Citations in the corpus

Unobtrusive Sensing and Wearable Devices for Health Informatics (2014, background): “Unobtrusive wearable devices for various physiological measurement developed by different groups: watch-type BP device [3], PPG sensors mounted on eyeglasses [37], motion assessment with sensors mounted on shoes [53], wireless ECG necklace for ambulatory cardiac monitoring (courtesy of IMEC, Netherl”
Optimal Placement of Accelerometers for the Detection of Everyday Activities (2013, methodology): “Atallah [2] Lying, walking, running, cycling, sitting, transitional (15) 11 Chest, upper arm, wrist, hip thigh, ankle, ear Variance, RMS, mean, energy, entropy, skewness, kurtosis, covariance kNN (na), Bayesian (na)”
On-body localization of wearable devices: An investigation of position-aware activity recognition (2016, background): “Further studies have shown that the optimal sensor placement depends on the activity to be recognized [6].”
Window Size Impact in Human Activity Recognition (2014, methodology): “The identiﬁcation of initial and end points could be also approached by leveraging the user feedback.”
Advanced internet of things for personalised healthcare systems: A survey (2017, background): “Many researches[36]–[38]focus on developing some novel accurate, reliable cost-effective and non-invasive sensing techniques for an automatic collection of human health data in IoT based uncontrolled environments The networking layer for PHS is responsible to connect all devices in sensory layer…”
Activity recognition using a single accelerometer placed at the wrist or ankle (2013, background): “For example, one study that compared different sensor placement sites reported lower activity type detection performance for the wrist with respect to other locations for the detection of sit-to-stand or lie-to-stand transitions (2).”
A Review of Wearable Technologies for Elderly Care that Can Accurately Track Indoor Position, Recognize Physical Activities and Monitor Vital Signs in Real Time (2017, methodology): “…data-driven approaches are the Hidden Markov Model (HMM) [57], k -Nearest Neighbor algorithm (KNN) [13,60,68,74,85,86], Principal Component Analysis (PCA) [87], Naïve Bayes (NB) [13,59,60,74,86], Gaussian Mixture Model (GMM) [58], Random Forest (RF) [85], Decision Tree [13,59,74,86], and so on.”
A Survey on Activity Detection and Classification Using Wearable Sensors (2017, neutral): “[32], Rednic et al.”