Commit c6dbabe4 authored by burcharr's avatar burcharr 💬
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......@@ -103,7 +103,7 @@ Hand washing compliance can be measured using different tools. Jain et al. @jain
A study by Li et al. @li_wristwash_2018 is able to recognize 13 steps of a hand washing procedure with an accuracy of $85\,\%$. They employ a sliding window feature based hidden markov model approach. Wang et al. explore using sensor armbands to assess the users compliance with given hand washing hygiene guidelines @wang_accurate_2020. They run a classifier using XGBoost and are mostly able to separate the different steps of the scripted hand washing routine.
Added to that, Cao et al. @cao_awash_2021 developed a system that similarly detects different steps of a scripted hand washing routine and prompts the user, if they confuse the order of the steps or forget one of the steps. The technology is aimed at elderly patients with dementia. Their system is able to detect which step of hand washing is currently conducted based on wrist motion data using an LSTM based neural network. However, none of the three systems mentioned in this paragraph are meant to separate hand washing from other activities.
Mondol et al. employ a simple feed forward neural network consisting of a few linear layers to detect hand washing @sayeed_mondol_hawad_2020. Their method seeks to specifically eliminate false positives by trying to detect out of distribution (OOD) samples, i.e. samples that are very different from the ones seen by the model during training. They apply a conditional Gaussian distribution of the network's features of the last layer before the output layer (penultimate layer).
In order to separate hand washing from other activities, Mondol et al. employ a simple feed forward neural network. Their network consists of a few linear layers and can be used to detect hand washing @sayeed_mondol_hawad_2020. Their method seeks to specifically eliminate false positives by trying to detect out of distribution (OOD) samples, i.e. samples that are very different from the ones seen by the model during training. They apply a conditional Gaussian distribution of the network's features of the last layer before the output layer (penultimate layer).
![Steps of HAWAD for parameter estimation and inference, taken from @sayeed_mondol_hawad_2020](img/HAWAD_filter.png){width=98% #fig:HAWAD}
......@@ -117,4 +117,4 @@ D_M(\mathbf{x}) = \sqrt{(\mathbf{x}- \boldsymbol{\mu})^T\mathbf{S}^{-1}(\mathbf{
\caption*{Equation \ref*{eqn:mahala}: Mahalanobis distance}
\end{figure}
To our knowledge, no hand washing detection method using more complicated neural networks has been published as of 2021. The performance reached for the HAWAD paper could possibly be surpassed by convolutional or recurrent networks or a combination thereof, i.e. DeepConvLSTM. Added to that, the detection and separation of compulsive hand washing from ordinary hand washing has, to our knowledge, never been done before, it seems likely, that methods from hand washing detection and human activity recognition can be applied to this problem as well.
To our knowledge, no hand washing detection method using more complicated neural networks than fully connected networks has been published as of 2021. The performance reached for the HAWAD paper could possibly be surpassed by convolutional or recurrent networks or a combination thereof, i.e. DeepConvLSTM. Added to that, the detection and separation of compulsive hand washing from ordinary hand washing has, to our knowledge, never been done before, it seems likely, that methods from hand washing detection and human activity recognition can be applied to this problem as well.
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