The MobiAct Dataset: Recognition of Activities of Daily Living using Smartphones

By George Vavoulas, Charikleia Chatzaki, Thodoris Malliotakis, M. Pediaditis, Manolis Tsiknakis. ICT4AgeingWell, 2016

296 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Demography, Transportation

View Paper: https://www.semanticscholar.org/paper/b46f04b982f7724aeabe093e62b46402ac982f36

How researchers see this paper

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

In Computer Vision and Pattern Recognition

1. Hamed Haddadi
2. Andrea Cavallaro
3. Richard G. Clegg
4. Mohammad Malekzadeh
5. Xu Chen
6. Qiong Wu
7. Zhi Zhou
8. Kaiwen He

In Demography

1. Xu Chen
2. Parisa Rashidi
3. Qiong Wu

In Transportation

1. Zhi Zhou
2. Xu Chen
3. Parisa Rashidi
4. Qiong Wu
5. Diane Joyce Cook

Citations in the corpus

• Human Activity Recognition using Inertial, Physiological and Environmental Sensors: A Comprehensive Survey (2020, methodology): “The third and fourth most used model are the Decision Tree (DT) model [6], [78], [85], [94], [95], [113], [136], [142], [145], [159], [165], [173], [177], [178], [184], [185], [193], [208], [210], [212], which was used in 19 papers, obtaining an average accuracy of 94.”
• FedHome: Cloud-Edge Based Personalized Federated Learning for In-Home Health Monitoring (2020, methodology): “There are 57 volunteers (42 men and 15 women) within an age bracket of 20-47 years participating in the generation of MobiAct dataset.”
• UniMiB SHAR: A Dataset for Human Activity Recognition Using Acceleration Data from Smartphones (2016, unknown): “MobiAct [20] 2016 yes yes 57 15 42 20 - 47 160 - 193 50 - 120 25 ± 4 175 ± 4 76.”
• Deep Learning in Human Activity Recognition with Wearable Sensors: A Review on Advances (2021, methodology): “Both the publicly available MobiAct [284] and a new dataset, MotionSense, are used to evaluate the proposed framework’s efficacy.”
• Protecting Sensory Data against Sensitive Inferences (2018, methodology): “MobiAct [15] includes sensor data (accelerometer, gyroscope, orientation) from a smartphone collected when data subjects performed nine activities and a range of falls.”
• Personalized Federated Learning for Intelligent IoT Applications: A Cloud-Edge Based Framework (2020, methodology): “In the experiments, we focus on human activity recognition task based on a publicly accessible dataset called MobiAct [26].”