Detection of Daily Activities and Sports With Wearable Sensors in Controlled and Uncontrolled Conditions

By Miikka Ermes, Juha Pärkkä, Jani Mäntyjärvi, Ilkka Korhonen. IEEE Transactions on Information Technology in Biomedicine, 2008

770 citations recorded by the source corpus.

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

View Paper: https://www.semanticscholar.org/paper/e6c2482e43099125e2809d785c99e6687fc8c550

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In Biomedical Engineering

In Computer Vision and Pattern Recognition

In Physiology

Citations in the corpus

A Triaxial Accelerometer-Based Physical-Activity Recognition via Augmented-Signal Features and a Hierarchical Recognizer (2010, background): “Most of these systems investigated the use of multiple accelerometers attached to different sites on a subject's body [2], [3], [5], [8], [10], [11], [15], [17]–[21].”
Multicolumn Bidirectional Long Short-Term Memory for Mobile Devices-Based Human Activity Recognition (2016, background): “As a result, mobile device-based human activity recognition systems (MARSs) have received increasing attention over recent years in the field of Internet of Things, including social life networks [18], [19], [44], wearables [6], [8], [37], [45], health care [1], [22], [32], and so on [23], [25], [31”
Creating and benchmarking a new dataset for physical activity monitoring (2012, background): “in [5, 11]), since an extra IMU on the thigh would be needed for a reliable differentiation of these postures.”
Direct measurement of human movement by accelerometry. (2008, background): “[81] Ermes M, Parkka J, Mantyjarvi J, Korhonen I.”
Comparative study on classifying human activities with miniature inertial and magnetic sensors (2010, methodology): “In LSM, the decision threshold is chosen as the distance between a test vector and the average reference vector of each class; and in ANN, the norm of the difference between the desired and actual outputs.”
Human Activity Recognition Using Inertial/Magnetic Sensor Units (2010, methodology): “Inertial sensor based activity recognition systems are used in monitoring and observation of the elderly remotely by personal alarm systems [8], detection and classification of falls [9,10], medical diagnosis and treatment [11], monitoring children remotely at home or in school, rehabilitation and p”
Wearable Sensors and Systems (2010, background): “These considerations justify the initial focus of researchers and developers in the field of wearable sensors and systems on wellness [28]–[30] and activity monitoring [31]–[36].”
Wearable Sensors for Human Activity Monitoring: A Review (2015, neutral): “using wearable sensors [7]–[9].”
Recognizing Detailed Human Context in the Wild from Smartphones and Smartwatches (2016, background): “In many studies the recognition setup involved cumbersome devices that had to be worn in a specific way [5], [12], [13], [14], [15], [17], [22], [23], [25], [28], [29].”
Understanding mobility based on GPS data (2008, methodology): “RELATED WORKS Solutions based on multiple sensors: In paper [4] and [9], Parkka et al.”
Assessment of physical activity in youth. (2008, background): “More recently, activitytype classification schemes for interpretation of accelerometry data have emerged that aim to detect a multidimensional movement signature and assign membership to a (limited) set of activity types (7, 37, 48, 75, 79, 122).”
Wearable Environmental Sensors and Infrastructure for Mobile Large-Scale Urban Deployment (2016, background): “various sporting activities in naturalistic non-laboratory conditions [23].”