Comparative study on classifying human activities with miniature inertial and magnetic sensors

By Kerem Altun, Billur Barshan, Orkun Tunçel. Pattern Recognition, 2010

598 citations recorded by the source corpus.

Subfields: Biomedical Engineering, Computer Vision and Pattern Recognition, Human-Computer Interaction

View Paper: https://www.semanticscholar.org/paper/2c413ddb38b24bd96990ea597e8f6dae75856366

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

1. Murat Cihan Yüksek
2. Billur Barshan
3. Nirmalya Roy
4. Sreenivasan Ramasamy Ramamurthy
5. Kerem Altun
6. Samer Mohammed
7. Yacine Amirat
8. Nasser Kehtarnavaz

In Computer Vision and Pattern Recognition

1. Sreenivasan Ramasamy Ramamurthy
2. Nirmalya Roy
3. Murat Cihan Yüksek
4. Billur Barshan
5. Kerem Altun
6. Shen Zhang
7. Yuanbei Deng
8. Yaxuan Li

In Human-Computer Interaction

1. Murat Cihan Yüksek
2. Sreenivasan Ramasamy Ramamurthy
3. Nirmalya Roy
4. Billur Barshan
5. Kerem Altun
6. Samer Mohammed
7. Yacine Amirat

Citations in the corpus

• Wearable Devices in Medical Internet of Things: Scientific Research and Commercially Available Devices (2017, background): “A few 9DoF wireless inertial measurement units (IMUs) are commercially available and have been used for research projects [23-25].”
• Physical Human Activity Recognition Using Wearable Sensors (2015, background): “In this section, we present a background on wearable sensor’s placement, pre-processing data including feature extraction and selection and classification techniques used in the field of human activity recognition.”
• Deep Learning in Human Activity Recognition with Wearable Sensors: A Review on Advances (2021, methodology): “6 50–200 Hz 37 Daily & Sports Activities [19] Locomotion 9D IMU 19 25 Hz 37 MHEALTH [30, 31] Locomotion & gesture 9D IMU & ECG 12 50 Hz 33 Opportunity [59] Locomotion & gesture 9D IMU 16 50 Hz 32 PAMAP2 [228] Locomotion & activities 9D IMU & HR monitor 18 100 Hz 32 Daphnet [23] Freezing of gait 3D A”
• Human Activity Recognition Using Inertial/Magnetic Sensor Units (2010, methodology): “In accordance with the confusion matrices and ROC curves in [28], incorrectly classified activities are mostly A7, A8, A9, A10, and A11.”
• Recognizing Daily and Sports Activities in Two Open Source Machine Learning Environments Using Body-Worn Sensor Units (2014, methodology): “(a) All eigenvalues (1170) and (b) the first 50 eigenvalues of the covariance matrix sorted in descending order [16].”
• Recent trends in machine learning for human activity recognition—A survey (2018, methodology): “Ying, Lin, Tseng, and Hsieh (2015) have proposed a transfer learning model on high-variety data (data from different sources) which is validated using statistical hypothesis Kolmogorov – Smirnov and χ 2 goodness of fit test, and evaluated on Walk8, HAR (Anguita et al., 2013), and DaSA (Altun, Barsha”