Introducing a New Benchmarked Dataset for Activity Monitoring

By Attila Reiss, Didier Stricker. 2012

1,406 citations recorded by the source corpus.

Subfields: Biomedical Engineering, Computer Vision and Pattern Recognition, Signal Processing

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 Signal Processing

Citations in the corpus

Extremely Fast Decision Tree (2018, background): “9: PAMAP2 Activity Recognition dataset (UCI)– 9 subjects data combined [21, 22] Research Track Paper KDD 2018, August 19‒23, 2018, London, United Kingdom”
DBSCAN Revisited, Revisited (2017, methodology): “(See Figure 6(c) for the “Farm” dataset and Figure 8(b) for the “PAMAP2” dataset.)”
Deep Learning in Human Activity Recognition with Wearable Sensors: A Review on Advances (2021, methodology): “The accuracy on UCI-HAR, WISDM[155], and PAMAP2[228] datasets achieved 96.”
Stratified Transfer Learning for Cross-domain Activity Recognition (2017, neutral): “PAMAP2 dataset ( PAMAP2 ) [43] is collected by 9 subjects performing 18 activities with sensors on 3 body parts.”
Unsupervised Deep Anomaly Detection for Multi-Sensor Time-Series Signals (2021, methodology): “PAMAP2 [61] Activity Recognition 1,140,000 27 18 Public CAP [62] Sleep Stage Detection 921,700,000 21 8 Public Mental Fatigue Dataset [2] Fatigue Detection 1,458,648 4 2 Private ZHANG ETAL.”
UTiLearn: A Personalised Ubiquitous Teaching and Learning System for Smart Societies (2017, background): “The datasets have not been given a specific name by their originators except the PAM (Reiss and Stricker [78]) and Opportunity (Roggen et al.”
Multi-input CNN-GRU based human activity recognition using wearable sensors (2021, methodology): “UCI-HAR [3], WISDM [23], and PAMAP2 [39] are applied to exhibit the superior performance of the proposed classifier.”
A Semisupervised Recurrent Convolutional Attention Model for Human Activity Recognition (2020, background): “After eliminating the impact of “others”, the overall classiﬁcation performance on MHEALTH and PAMAP2 is boosted.”
Recent trends in machine learning for human activity recognition—A survey (2018, methodology): “In HAR systems designed using shallow learning, the frequently used feature heuristics are dependent on the domain knowledge of the researcher and the performance of the machine learning techniques is highly dependent on the data representation (Bengio, 2013 Hammerla, Halloran, and Ploetz (2016) hav”
Creating and benchmarking a new dataset for physical activity monitoring (2012, background): “The key facts of the created dataset, and the key results of an initial classification are presented in [17].”
Jointly Learning Energy Expenditures and Activities Using Egocentric Multimodal Signals (2017, neutral): “4 7 320×240 Head X - - Indoor Activity PAMAP2 [52] 9 - - - - X X -”