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......@@ -467,16 +467,6 @@ Conclusions: A sensor armband can be used to measure hand hygiene quality relati
file = {Full Text PDF:/home/robin/Zotero/storage/VFNBDWYC/Paszke et al. - 2019 - PyTorch An Imperative Style, High-Performance Dee.pdf:application/pdf},
}
@article{pedregosa_scikit-learn_nodate,
title = {Scikit-learn: Machine Learning in Python},
abstract = {Scikit-learn is a Python module integrating a wide range of state-of-the-art machine learning algorithms for medium-scale supervised and unsupervised problems. This package focuses on bringing machine learning to non-specialists using a general-purpose high-level language. Emphasis is put on ease of use, performance, documentation, and {API} consistency. It has minimal dependencies and is distributed under the simplified {BSD} license, encouraging its use in both academic and commercial settings. Source code, binaries, and documentation can be downloaded from http://scikit-learn.sourceforge.net.},
pages = {6},
journaltitle = {{MACHINE} {LEARNING} {IN} {PYTHON}},
author = {Pedregosa, Fabian and Varoquaux, Gael and Gramfort, Alexandre and Michel, Vincent and Thirion, Bertrand and Grisel, Olivier and Blondel, Mathieu and Prettenhofer, Peter and Weiss, Ron and Dubourg, Vincent and Vanderplas, Jake and Passos, Alexandre and Cournapeau, David},
langid = {english},
file = {Pedregosa et al. - Scikit-learn Machine Learning in Python.pdf:/home/robin/Zotero/storage/ASR7U3AY/Pedregosa et al. - Scikit-learn Machine Learning in Python.pdf:application/pdf},
}
@article{janocha_loss_2017,
title = {On Loss Functions for Deep Neural Networks in Classification},
url = {http://arxiv.org/abs/1702.05659},
......@@ -812,7 +802,7 @@ Type: article},
date = {2018-02-23},
eprinttype = {arxiv},
eprint = {1711.00489},
keywords = {Computer Science - Computer Vision and Pattern Recognition, Computer Science - Distributed, Parallel, and Cluster Computing, Computer Science - Machine Learning, Statistics - Machine Learning},
keywords = {Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning, Statistics - Machine Learning, Computer Science - Distributed, Parallel, and Cluster Computing},
file = {arXiv Fulltext PDF:/home/robin/Zotero/storage/MIRRJ2GY/Smith et al. - 2018 - Don't Decay the Learning Rate, Increase the Batch .pdf:application/pdf;arXiv.org Snapshot:/home/robin/Zotero/storage/PLS2IGLT/1711.html:text/html},
}
......@@ -865,6 +855,80 @@ Type: article},
date = {2021-03-15},
eprinttype = {arxiv},
eprint = {2005.00698},
keywords = {Computer Science - Human-Computer Interaction, Computer Science - Machine Learning, Electrical Engineering and Systems Science - Signal Processing},
keywords = {Computer Science - Machine Learning, Computer Science - Human-Computer Interaction, Electrical Engineering and Systems Science - Signal Processing},
file = {arXiv Fulltext PDF:/home/robin/Zotero/storage/YG29NC8S/Singh et al. - 2021 - Deep ConvLSTM with self-attention for human activi.pdf:application/pdf;arXiv.org Snapshot:/home/robin/Zotero/storage/59LRXID7/2005.html:text/html},
}
@article{pedregosa_scikit-learn_nodate,
title = {Scikit-learn: Machine Learning in Python},
abstract = {Scikit-learn is a Python module integrating a wide range of state-of-the-art machine learning algorithms for medium-scale supervised and unsupervised problems. This package focuses on bringing machine learning to non-specialists using a general-purpose high-level language. Emphasis is put on ease of use, performance, documentation, and {API} consistency. It has minimal dependencies and is distributed under the simplified {BSD} license, encouraging its use in both academic and commercial settings. Source code, binaries, and documentation can be downloaded from http://scikit-learn.sourceforge.net.},
pages = {6},
journaltitle = {{MACHINE} {LEARNING} {IN} {PYTHON}},
author = {Pedregosa, Fabian and Varoquaux, Gael and Gramfort, Alexandre and Michel, Vincent and Thirion, Bertrand and Grisel, Olivier and Blondel, Mathieu and Prettenhofer, Peter and Weiss, Ron and Dubourg, Vincent and Vanderplas, Jake and Passos, Alexandre and Cournapeau, David},
langid = {english},
file = {Pedregosa et al. - Scikit-learn Machine Learning in Python.pdf:/home/robin/Zotero/storage/9ZFWISBC/Pedregosa et al. - Scikit-learn Machine Learning in Python.pdf:application/pdf},
}
@article{kwapisz_activity_2011,
title = {Activity recognition using cell phone accelerometers},
volume = {12},
issn = {1931-0145, 1931-0153},
url = {https://dl.acm.org/doi/10.1145/1964897.1964918},
doi = {10.1145/1964897.1964918},
abstract = {Mobile devices are becoming increasingly sophisticated and the latest generation of smart cell phones now incorporates many diverse and powerful sensors. These sensors include {GPS} sensors, vision sensors (i.e., cameras), audio sensors (i.e., microphones), light sensors, temperature sensors, direction sensors (i.e., magnetic compasses), and acceleration sensors (i.e., accelerometers). The availability of these sensors in mass-marketed communication devices creates exciting new opportunities for data mining and data mining applications. In this paper we describe and evaluate a system that uses phone-based accelerometers to perform activity recognition, a task which involves identifying the physical activity a user is performing. To implement our system we collected labeled accelerometer data from twenty-nine users as they performed daily activities such as walking, jogging, climbing stairs, sitting, and standing, and then aggregated this time series data into examples that summarize the user activity over 10second intervals. We then used the resulting training data to induce a predictive model for activity recognition. This work is significant because the activity recognition model permits us to gain useful knowledge about the habits of millions of users passively—just by having them carry cell phones in their pockets. Our work has a wide range of applications, including automatic customization of the mobile device’s behavior based upon a user’s activity (e.g., sending calls directly to voicemail if a user is jogging) and generating a daily/weekly activity profile to determine if a user (perhaps an obese child) is performing a healthy amount of exercise.},
pages = {74--82},
number = {2},
journaltitle = {{ACM} {SIGKDD} Explorations Newsletter},
shortjournal = {{SIGKDD} Explor. Newsl.},
author = {Kwapisz, Jennifer R. and Weiss, Gary M. and Moore, Samuel A.},
urldate = {2021-10-25},
date = {2011-03-31},
langid = {english},
file = {Kwapisz et al. - 2011 - Activity recognition using cell phone acceleromete.pdf:/home/robin/Zotero/storage/BXY5GASY/Kwapisz et al. - 2011 - Activity recognition using cell phone acceleromete.pdf:application/pdf},
}
@inproceedings{sztyler_-body_2016,
title = {On-body localization of wearable devices: An investigation of position-aware activity recognition},
doi = {10.1109/PERCOM.2016.7456521},
shorttitle = {On-body localization of wearable devices},
abstract = {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 is not known a priori. We present a new real world data set that has been collected from 15 participants for 8 common activities were they carried 7 wearable devices in different positions. Further, we introduce a device localization method that uses random forest classifiers to predict the device position based on acceleration data. We perform the most complete experiment in on-body device location that includes all relevant device positions for the recognition of a variety of different activities. We show that the method outperforms other approaches achieving an F-Measure of 89\% across different positions. We also show that the detection of the device position consistently improves the result of activity recognition for common activities.},
eventtitle = {2016 {IEEE} International Conference on Pervasive Computing and Communications ({PerCom})},
pages = {1--9},
booktitle = {2016 {IEEE} International Conference on Pervasive Computing and Communications ({PerCom})},
author = {Sztyler, Timo and Stuckenschmidt, Heiner},
date = {2016-03},
keywords = {Acceleration, Biomedical monitoring, Context, Feature extraction, Gravity, Performance evaluation, Sensors},
file = {IEEE Xplore Full Text PDF:/home/robin/Zotero/storage/8A7UCP3G/Sztyler and Stuckenschmidt - 2016 - On-body localization of wearable devices An inves.pdf:application/pdf;IEEE Xplore Abstract Record:/home/robin/Zotero/storage/ZA3NXXR7/7456521.html:text/html},
}
@inproceedings{banos_benchmark_2012,
location = {New York, {NY}, {USA}},
title = {A benchmark dataset to evaluate sensor displacement in activity recognition},
isbn = {978-1-4503-1224-0},
url = {https://doi.org/10.1145/2370216.2370437},
doi = {10.1145/2370216.2370437},
series = {{UbiComp} '12},
abstract = {This work introduces an open benchmark dataset to investigate inertial sensor displacement effects in activity recognition. While sensor position displacements such as rotations and translations have been recognised as a key limitation for the deployment of wearable systems, a realistic dataset is lacking. We introduce a concept of gradual sensor displacement conditions, including ideal, self-placement of a user, and mutual displacement deployments. These conditions were analysed in the dataset considering 33 fitness activities, recorded using 9 inertial sensor units from 17 participants. Our statistical analysis of acceleration features quantified relative effects of the displacement conditions. We expect that the dataset can be used to benchmark and compare recognition algorithms in the future.},
pages = {1026--1035},
booktitle = {Proceedings of the 2012 {ACM} Conference on Ubiquitous Computing},
publisher = {Association for Computing Machinery},
author = {Baños, Oresti and Damas, Miguel and Pomares, Héctor and Rojas, Ignacio and Tóth, Máté Attila and Amft, Oliver},
urldate = {2021-10-25},
date = {2012-09-05},
keywords = {activity recognition, benchmark dataset, fitness exercises, motion sensors, sensor displacement},
file = {Full Text PDF:/home/robin/Zotero/storage/KXDSSXY5/Baños et al. - 2012 - A benchmark dataset to evaluate sensor displacemen.pdf:application/pdf},
}
@inproceedings{reiss_introducing_2012,
title = {Introducing a New Benchmarked Dataset for Activity Monitoring},
doi = {10.1109/ISWC.2012.13},
abstract = {This paper addresses the lack of a commonly used, standard dataset and established benchmarking problems for physical activity monitoring. A new dataset - recorded from 18 activities performed by 9 subjects, wearing 3 {IMUs} and a {HR}-monitor - is created and made publicly available. Moreover, 4 classification problems are benchmarked on the dataset, using a standard data processing chain and 5 different classifiers. The benchmark shows the difficulty of the classification tasks and exposes new challenges for physical activity monitoring.},
eventtitle = {2012 16th International Symposium on Wearable Computers},
pages = {108--109},
booktitle = {2012 16th International Symposium on Wearable Computers},
author = {Reiss, Attila and Stricker, Didier},
date = {2012-06},
note = {{ISSN}: 2376-8541},
keywords = {Accuracy, Benchmark testing, Biomedical monitoring, Decision trees, Heart rate, Monitoring, Standards},
file = {IEEE Xplore Full Text PDF:/home/robin/Zotero/storage/2SGLX27L/Reiss and Stricker - 2012 - Introducing a New Benchmarked Dataset for Activity.pdf:application/pdf;IEEE Xplore Abstract Record:/home/robin/Zotero/storage/FC9A9P9I/6246152.html:text/html},
}
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......@@ -40,7 +40,7 @@ In this work, we want to develop a method for the real time detection of hand wa
We want to show that neural network based classification methods can be applied to the recognition of hand washing. We want to base our method on sensor data from inertial measurement sensors in smart watches or other wrist worn IMU-equipped devices. We want to detect the hand washing in real time and directly on the mobile, i.e. wrist wearable device, such as a smart watch. Doing so, we would be able to give instant real time feedback to the user of the device.
### Separation of hand washing and compulsive hand washing
Added to the detection of hand washing, the detection of obsessive-compulsive hand washing is part of our goals. We want to be able to separate obsessive hand washing from hand washing, based on the inertial motion data.
Added to the detection of hand washing, the detection of obsessive-compulsive hand washing is part of our goals. We want to be able to separate compulsive hand washing from non compulsive hand washing, based on the inertial motion data. Especially for the scenario of possible interventions used for the treatment of OCD, this separation is crucial, as patients do also wash their hands in non compulsive ways.
### Real world evaluation
We want to evaluate the most promising of the developed models in a real world evaluation, in order to obtain a realistic estimate of its applicability in the task of hand washing detection. We want to report results of an evaluation with multiple subjects to obtain a meaningful performance estimation.
\ No newline at end of file
We want to evaluate the most promising of the developed models in a real world evaluation, in order to obtain a realistic estimate of its applicability in the task of hand washing detection. We want to report results of an evaluation with multiple subjects to obtain a meaningful performance estimation. From this estimation we want to draw conclusions on the applicability of the developed system in real world therapy scenarios.
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......@@ -35,13 +35,28 @@ In order to correctly detect the hand washing in real time in a real world scena
In order to also separate non-obsessive hand washing from obsessive hand washing, data of obsessive hand washing must be included. In order to record this data, real patients can be asked to wear a sensor during their daily life.
### Data used in our data set
We used hand washing data recorded at the University of Basel and University of Freiburg as our "positive" class data. This data was recorded at several occasions and using different paradigms. % TODO: list our data sets. Obsessive/Non obsessive, fake, mention study.
We recorded at 50Hz. TODO: app, credits (reference?? automotion, alex app usw.)
We used hand washing data and "compulsive" hand washing data recorded at the University of Basel and University of Freiburg as our "positive" class data. This data was recorded at several occasions and using different paradigms. We mainly used data recorded at 50Hz, using a smart watch application. On several occasions in 2019 and 2020, data was recorded. The data from 2019 includes hand washing data and, added to that, also includes simulated "compulsive" hand washing. For the simulated compulsive hand washing, subjects were asked to ... todo ask phil for protocolls whatever.
The recording and labeling of the data is not part of this work
Added to that, multiple data sets from other studies were used. In our selection, we include publicly available data sets of which each contains wrist worn sensor data of at least one arm. Not all the data sets were recorded at the frequency of 50Hz. Thus, we resampled all data obtained to our fixed frequency using linear interpolation.
\begin{table}[]
\begin{tabular}{|l|l|l|l|}
\hline
No & Dataset name & Contained activities (excerpt) & Original recording frequency \\ \hline
1 & 2019 & hand washing and compulsive hand washing & 50 Hz \\ \hline
2 & 2020 & different hand washing activities & 50 Hz \\ \hline
3 & 2020 long-term & All day recordings of activities of daily living & 50 Hz \\ \hline
4 & WISDM @kwapisz\_activity\_2011 & Movement (walking, jogging, stairs, sitting, ...) & 20 Hz \\ \hline
5 & RealWorld @sztyler\_-body\_2016 & Movement (walking, jogging, stairs, sitting, ...) & 50 Hz \\ \hline
6 & REALDISP @banos\_benchmark\_2012 & Movement and fitness exercises & 50 Hz \\ \hline
7 & PAMAP2 @reiss\_introducing\_2012 & Movement, sports, household chores, desk work & 100 Hz \\ \hline
\end{tabular}
\caption{Data sets used in our combined data set. Data sets 1 to 3 stem from Freiburg / Basel, the rest are external data sets.}
\end{table}
The external data sets used and their specifications are listed in table %todo {add table with that info}.
The data sets were collected and converted by Daniel Homm, analyzed and resampled by us.
The external data sets were collected and converted by Daniel Homm, analyzed and resampled by us.
### Specifications of the resulting data set %TODO
The final data set used contains a total of x data points. Of these x data points, y were hand washing, z were other activities or idle. Out of the y hand washing data points, w were obsessive hand washing data points. Table k shows the specific sizes and the comparisons. Researchers willing to use this data set should note that for most machine learning methods it makes sense to balance the training set with regard to the classes, in order to avoid biases towards the more frequent classes. In specific machine learning algorithms, one could also combat the class imbalance problem using an importance weighting for the different classes. To train a neural network, the loss function can also be weighted by the class frequency.
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