Recognition of activities of daily living (ADLs) is an enabling technology for several ubiquitous computing applications. In this field, most activity recognition systems rely on supervised learning to extract activity models from labeled datasets. A problem with that approach is the acquisition of comprehensive activity datasets, which ...
This page provides the results of our experiments. Each result file contains the test results for each individual patient/
This work is an extension. Please consider the following page for detailed results concerning Offline POLARIS: Unsupervised Recognition of Interleaved Activities of Daily Living through Ontological and Probabilistic Reasoning.
MLNnc solver: show
Ontology: show (unchanged compared to D. Riboni et al. 2016)
Smart-home activity recognition is an enabling tool for a wide range of ambient assisted living applications. The recognition of ADLs usually relies on supervised learning or knowledge-based reasoning techniques. In order to overcome the well-known limitations of those two approaches and, at the same time, to combine their strengths to ...
This page provides additional material. Those belong to the publication Modeling and Reasoning with ProbLog: An Application in Recognizing Complex Activities.
ProbLog Online Editor: show
This work was a cooperation between the
University of Milano
and the
University of Mannheim
Ambient Assisted Living using mobile device sensors is an active area of research in pervasive computing. In our work, we aim to implement a self-adaptive pervasive fall detection approach that is suitable for real life situations. The paper focuses on the problem that the device’s on-body position but also the falling pattern of a person ...
This page provides the results of our experiments. Each result file contains the individual results, i.e., not aggregated. The results were analyzed with pivot tables in Excel. These results belong to the publication Hips Do Lie! A Position-Aware Mobile Fall Detection System.
Following, the datasets which we considered in our experiments can be found. Source provides a link to the original dataset, Publication provides a link to the related publication, and Download provides our preprocessed version of the original dataset. Further information can be found here (DataSets) and here (Traces).
This work was a cooperation between the
Chair of Information Systems II
and the
Human activity recognition using wearable devices is an active area of research in pervasive computing. In our work, we target patients and elders which are unable to collect and label the required data for a subject-specific approach. For that purpose, we focus on the problem of cross-subjects based recognition models and introduce an ...
This page provides the results of our experiments. Each result file contains the test results (Precision, Recall, F-measure) for each individual subjects. However, the results are not aggregated, i.e., does not provide average values of all subjects, positions, or combinations. These results belong to the publication Online Personalization of Cross-Subjects based Activity Recognition Models on Wearable Devices.
All experiments were performed using random forest as a classifier. We considered two different versions of this classifier: online and offline learning. The former was self/
Data Set | Short Description | Download | Size [MB] |
---|---|---|---|
#1 | Single sensor setup, separated by position and subject | Download | ~120 |
#2 | Two-part setup, separated by combination and subject | Download | ~690 |
#3 | Three-part setup, separated by combination and subject | Download | ~1600 |
#4 | Four-part setup, separated by combination and subject | Download | ~2100 |
#5 | Five-part setup, separated by combination and subject | Download | ~1600 |
#6 | Six-part setup, separated by combination and subject | Download | ~640 |
Test | Short Description | Related To | Result | Size [MB] |
---|---|---|---|---|
#1 | Randomy, One Accelerometer, Offline learning | Table II | show | ~1 |
#2 | Randomy, Two Accelerometer, Offline learning | Table II/ | show | ~2 |
#3 | Randomy, Three Accelerometer, Offline learning | Table II | show | ~3 |
#4 | Randomy, Four Accelerometer, Offline learning | Table II | show | ~3 |
#5 | Randomy, Five Accelerometer, Offline learning | Table II | show | ~2 |
#6 | Randomy, Six Accelerometer, Offline learning | Table II | show | ~1 |
#7 | L1O, One Accelerometer, Offline learning | Table II | show | <1 |
#8 | L1O, Two Accelerometer, Offline learning | Table II/ | show | <1 |
#9 | L1O, Three Accelerometer, Offline learning | Table II | show | <1 |
#10 | L1O, Four Accelerometer, Offline learning | Table II | show | <1 |
#11 | L1O, Five Accelerometer, Offline learning | Table II | show | <1 |
#12 | L1O, Six Accelerometer, Offline learning | Table II | show | <1 |
#13 | Our approach, One Accelerometer, Offline learning | Table II | show | <1 |
#14 | Our approach, Two Accelerometer, Offline learning | Table II/ | show | <1 |
#15 | Our approach, Three Accelerometer, Offline learning | Table II | show | <1 |
#16 | Our approach, Four Accelerometer, Offline learning | Table II | show | <1 |
#17 | Our approach, Five Accelerometer, Offline learning | Table II | show | <1 |
#18 | Our approach, Six Accelerometer, Offline learning | Table II | show | <1 |
#19 | Subject-Specific, One Accelerometer, Offline learning | - | show | <1 |
#20 | Subject-Specific, Two Accelerometer, Offline learning | - | show | <1 |
Test | Short Description | Related To | Result | Size [MB] |
---|---|---|---|---|
#1 | Our approach, One Accelerometer, Online Learning | - | show | <1 |
#2 | Our approach + User-Feedback, One Accelerometer, Online Learning | - | show | ~1 |
#3 | Our approach + User-Feedback + Smoothing, One Accelerometer, Online Learning | - | show | ~1 |
#4 | Our approach, Two Accelerometer, Online Learning | Table V/ | show | <1 |
#5 | Our approach + Smoothing, Two Accelerometer, Online Learning | Table V/ | show | ~2 |
#6 | Our approach + User-Feedback, Two Accelerometer, Online Learning | Table V/ | show | ~2 |
#7 | Our approach + User-Feedback + Smoothing, Two Accelerometer, Online Learning | Table V/ | show | ~2 |
#8 | Our approach + User-Feedback + Smoothing (varying confidence threshold), Two Accelerometer, Online Learning | Figure 5 | show | ~23 |
#9 | Our approach + User-Feedback + Smoothing (varying number of trees), Two Accelerometer, Online Learning | Figure 6 | show | ~27 |
#10 | Subject-Specific, One Accelerometer, Online Learning | - | show | ~1 |
#11 | Subject-Specific, Two Accelerometer, Online Learning | - | show | ~2 |
Reliable human activity recognition with wearable devices enables the development of human-centric pervasive applications. We aim to develop a robust wearable-based activity recognition system for real life situations. Consequently, in this work we focus on the problem of recognizing the on-body position of the wearable device ensued by ...
This page provides the results of our experiments. Each result file contains the test results (F-Measure, Confusion Matrix, ...) for each individual subject. These results belong to the publication Position-Aware Activity Recognition with Wearable Devices.
This work is an extension. Please consider the following page for detailed results that correspond to Section 5.1–5.3: On-body Localization of Wearable Devices: An Investigation of Position-Aware Activity Recognition – (Numbers of tables have changed).
Test | Short Description | Related To | Result | Size [MB] |
---|---|---|---|---|
#1 | Dynamic Activity Recognition (Randomly, One accelormeter) | Table 10, 11 | show | <1 |
#2 | Dynamic Activity Recognition (L1O, One accelormeter) | Table 10, 11 | show | <1 |
#3 | Dynamic Activity Recognition (Top-Pairs, One accelormeter) | Table 10, 11 | show | <1 |
#4 | Dynamic Activity Recognition (Physical, One accelormeter) | Table 10, 11 | show | <1 |
#5 | Activity Recognition (Physical, One accelormeter) | Table 12 | show | <1 |
#6 | Activity Recognition (Physical, One accelormeter, including gravity feature for static activities) | Table 12 | show | <1 |
#7 | Activity Recognition (Physical, Two accelormeter, Only waist combinations) | Table 12, 13 | show | <1 |
#8 | Activity Recognition (Physical, Two accelormeter, Only waist combinations, including gravity feature for static activities) | Table 12, 13 | show | <1 |
#9 | Position Recognition (Randomly, One accelormeter) | Table 12, 13 | show | <1 |
#10 | Position Recognition (L1O, One accelormeter) | Table 12, 13 | show | <1 |
#11 | Position Recognition (Top-Pairs, One accelormeter) | Table 12, 13 | show | <1 |
#12 | Position Recognition (Physical, One accelormeter) | Table 12, 13 | show | <1 |
Currently, there is a trend to promote personalized health care in order to prevent diseases or to have a healthier life. Using current devices such as smart-phones and smart-watches, an individual can easily record detailed data from her daily life. Yet, this data has been mainly used for self-tracking in order to enable personalized ...
This page provides additional material of the publication Self-Tracking Reloaded: Applying Process Mining to Personalized Health Care from Labeled Sensor Data. In the following, we provide personal process maps and trace alignment clustering results of our experiments.
As mentioned in the paper, the XES files that are provided on this page were created from data sets that were created by other researchers. The original data set “Activity Log UCI” was created by Ordóñez et al. where hh102, hh104, and hh110 originate from Cook et. al.
Number | Short Description | Related To | Result |
---|---|---|---|
#1 | Main personal activity for all users during the working days (frequency) | 6.2 | show |
#2 | Main personal activity for all users during the weekend days (frequency) | 6.2 | show |
#3 | Main personal activity for all users during the working days (duration) | 6.2 | show |
#4 | Main personal activity for all users during the weekend days (duration) | 6.2 | show |
Number | Short Description | Related To | Result |
---|---|---|---|
#1 | Activity Log UCI Detailed (during the week) | 6.2 | show |
#2 | Activity Log UCI Detailed (Weekend) | 6.2 | show |
#3 | hh102 (during the week) | 6.2 | show |
#4 | hh102 (Weekend) | 6.2 | show |
#5 | hh104 (during the week) | 6.2 | show |
#6 | hh104 (Weekend) | 6.2 | show |
#7 | hh110 (during the week) | 6.2 | show |
#8 | hh110 (Weekend) | 6.2 | show |
Number | Short Description | Related To | Result |
---|---|---|---|
#1 | Clustered Traces, Subject 1 (based on our data set) | 7 | show |
Recognition of activities of daily living (ADLs) is an enabling technology for several ubiquitous computing applications. Most activity recognition systems rely on supervised learning methods to extract activity models from labeled datasets. An inherent problem of that approach consists in the acquisition of comprehensive activity datasets, ...
This page provides the results of our experiments. Each result file contains the test results for each individual patient/
Human activity recognition using mobile device sensors is an active area of research in pervasive computing. In our work, we aim at implementing activity recognition approaches that are suitable for real life situations. This paper focuses on the problem of recognizing the on-body position of the mobile device which in a real world setting ...
This page provides the results of our experiments. Each result file contains the test results (F-Measure, Confusion Matrix, ...) for each individual subjects. However, the results are not aggregated, i.e., does not provide average values of all subjects. We applied 10-fold cross validation and performed 10 runs where each time the data set was randomized and the 10-folds were recreated. These results belong to the publication On-body Localization of Wearable Devices: An Investigation of Position-Aware Activity Recognition.
Test | Short Description | Related To | Result |
---|---|---|---|
#1 | Position Detection (all activities) | Table II | show |
#2 | Position Detection (only static activities) | Table III | show |
#3 | Position Detection (only static activities) incl. gravity feature | Table IV | show |
#4 | Position Detection (only dynamic activities) | Table III | show |
#5 | Position Detection (activity-level dependend) | Table VI | see #3,#4 |
#6 | Activity Recognition (single classifier, all activities) | Table VII,VIII | show |
#7 | Activity Recognition (assumption: position is known for sure, all activities) | - | show |
#8 | Activity Recognition (based on the position detection result of RF, incl. all mistakes) | Table IX,X | show |
#9 | Distinction between static and dynamic activity (all activities) | Table V | show |
Test | Short Description | Related To | Result |
---|---|---|---|
#1 | Position Detection (all activities) | Table II | show |
#2 | Position Detection (only static activities) | Table III | show |
#3 | Position Detection (only static activities) incl. gravity feature | Table IV | show |
#4 | Position Detection (only dynamic activities) | Table III | show |
#5 | Position Detection (activity-level dependend) | Table VI | see #3,#4 |
#6 | Activity Recognition (single classifier, all activities) | Table VII,VIII | show |
#7 | Activity Recognition (assumption: position is known for sure, all activities) | - | show |
#8 | Activity Recognition (based on the position detection result of RF, incl. all mistakes) | Table IX,X | show |
#9 | Distinction between static and dynamic activity (all activities) | Table V | show |
Test | Short Description | Related To | Result |
---|---|---|---|
#1 | Position Detection (all activities) | Table II | show |
#2 | Position Detection (only static activities) | Table III | show |
#3 | Position Detection (only static activities) incl. gravity feature | Table IV | show |
#4 | Position Detection (only dynamic activities) | Table III | show |
#5 | Position Detection (activity-level dependend) | Table VI | see #3,#4 |
#6 | Activity Recognition (single classifier, all activities) | Table VII,VIII | show |
#7 | Activity Recognition (assumption: position is known for sure, all activities) | - | show |
#8 | Activity Recognition (based on the position detection result of RF, incl. all mistakes) | Table IX,X | show |
#9 | Distinction between static and dynamic activity (all activities) | Table V | show |
Test | Short Description | Related To | Result |
---|---|---|---|
#1 | Position Detection (all activities) | Table II | show |
#2 | Position Detection (only static activities) | Table III | show |
#3 | Position Detection (only static activities) incl. gravity feature | Table IV | show |
#4 | Position Detection (only dynamic activities) | Table III | show |
#5 | Position Detection (activity-level dependend) | Table VI | see #3,#4 |
#6 | Activity Recognition (single classifier, all activities) | Table VII,VIII | show |
#7 | Activity Recognition (assumption: position is known for sure, all activities) | - | show |
#8 | Activity Recognition (based on the position detection result of RF, incl. all mistakes) | Table IX,X | show |
#9 | Distinction between static and dynamic activity (all activities) | Table V | show |
Test | Short Description | Related To | Result |
---|---|---|---|
#1 | Position Detection (all activities) | Table II | show |
#2 | Position Detection (only static activities) | Table III | show |
#3 | Position Detection (only static activities) incl. gravity feature | Table IV | show |
#4 | Position Detection (only dynamic activities) | Table III | show |
#5 | Position Detection (activity-level dependend) | Table VI | see #3,#4 |
#6 | Activity Recognition (single classifier, all activities) | Table VII,VIII | show |
#7 | Activity Recognition (assumption: position is known for sure, all activities) | - | show |
#8 | Activity Recognition (based on the position detection result of RF, incl. all mistakes) | Table IX,X | show |
#9 | Distinction between static and dynamic activity (all activities) | Table V | show |
Test | Short Description | Related To | Result |
---|---|---|---|
#1 | Position Detection (all activities) | Table II | show |
#2 | Position Detection (only static activities) | Table III | show |
#3 | Position Detection (only static activities) incl. gravity feature | Table IV | show |
#4 | Position Detection (only dynamic activities) | Table III | show |
#5 | Position Detection (activity-level dependend) | Table VI | see #3,#4 |
#6 | Activity Recognition (single classifier, all activities) | Table VII,VIII | show |
#7 | Activity Recognition (assumption: position is known for sure, all activities) | - | show |
#8 | Activity Recognition (based on the position detection result of RF, incl. all mistakes) | Table IX,X | show |
#9 | Distinction between static and dynamic activity (all activities) | Table V | show |