Toothbrushing data and analysis of its potential use in human activity recognition applications

Hussain, Zawar; Waterworth, David; Aldeer, Murtadha; Zhang, Wei Emma; Sheng, Quan Z.

doi:10.1145/3419016.3431489

Cited by 10 publications

(9 citation statements)

References 15 publications

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“…Hence, we ended up with 9 brushing regions as follows: As it can be seen in Table 1, our proposed segment-based methods outperform the sample-based method proposed in [30] when applied on both their published dataset as well as our provided dataset. The difference in the performances of our model when applied on our dataset versus the dataset in [29], shows the challenging nature of brushing region detection when performed free-form versus under constraints (such as prescribed sequence of brushing, structured Bass brushing technique, etc.). While our models can achieve high k-fold cross-validation accuracy, one-subject-out classification accuracy is much more challenging.…”

Section: B Classification Resultsmentioning

confidence: 95%

“…b) Free-form: in which the participants were allowed to brush freely as they do normally. The datasets from previous studies were collected under several constraints, including restrained head and body movements or scripted brushing sequences [21], [22], [26], [29]. Including freestyle brushing in our dataset was essential for developing a brushing region detection algorithm appropriate for realworld brushing scenarios.…”

Section: Data Collection and Experimental Setupmentioning

confidence: 99%

“…Recently, Hussain et al [29], [30] published a dataset collected from accelerometer and gyroscope sensors attached to the brush handle during toothbrushing. They used 17 participants in their study, each brushing their teeth for one to five brushing sessions for a total of 64 brushing sessions.…”

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confidence: 99%

“…2) Propose a three-stage framework (i.e., pre-processing, brush transition times estimation, and time-series classification) to tackle the toothbrushing region detection problem. By applying our algorithm, both to our dataset as well as the dataset collected under the structured brushing assumption [29], we show that region detection during free-form brushing is more challenging than detection in controlled settings . 3) Show that a high classification accuracy can be achieved using a random train-test split (i.e.…”

mentioning

confidence: 99%

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Monitoring Brushing behaviors using Toothbrush Embedded Motion-Sensors

Essalat¹,

Padilla²,

Shetty³

et al. 2023

Preprint

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<p>Dental disease is largely preventable and closely linked to poor toothbrushing behaviors. Motion-sensors, such as accelerometers, gyroscopes, and magnetometers, allow for mon- itoring of toothbrushing behaviors. Researchers have attempted to infer tooth surface coverage using sensors attached to the toothbrush handle or embedded in smartwatches. However, the inferences may be deficient because the datasets were collected under structured toothbrushing assumptions performed in con- trolled laboratory settings and not the free-form and irregular brushing patterns observed in real-world settings. To address the aforementioned problem, we collected a dataset of 187 brush- ing sessions, including free-form brushing. We present, to our knowledge, the first motion-sensor dataset obtained during free- form brushing. Using our experiences, we discuss the challenges of studying toothbrushing behaviors in naturalistic settings. We also propose a three-stage method (i.e. pre-processing, brush transition time detection, and time-series classification) to detect the teeth surfaces brushed during a session. Our findings are two-fold: (a) the classification of teeth surfaces during free- form toothbrushing is more challenging than during brushing in controlled settings; (b) high classification accuracy can be achieved using random train-test split of the data (i.e. k-fold cross-validation); however, generalization beyond the participants in the training set poses difficulties. Beyond publishing the first dataset of free-form toothbrushing, we validate our findings by applying our proposed method to our provided dataset, as well as the datasets of toothbrushing in controlled settings. </p>

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Section: B Classification Resultsmentioning

confidence: 95%

Section: Data Collection and Experimental Setupmentioning

confidence: 99%

mentioning

confidence: 99%

mentioning

confidence: 99%

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Monitoring Brushing behaviors using Toothbrush Embedded Motion-Sensors

Essalat¹,

Padilla²,

Shetty³

et al. 2023

Preprint

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“…3,4 The introduction of low-cost sensor cameras 5 brings up new research opportunities for contactless human-computer interaction (HCI) in various applications such as robotics, healthcare, entertainment, intelligent surveillance, and intelligent environments. 6 Human hand gestures and dynamic signature recognition are becoming prevalent. This work proposes a hand gesture signature recognition system with the capability to recognize the identity of a person in a touchless acquisition environment.…”

Section: Introductionmentioning

confidence: 99%

In-air Hand Gesture Signature Recognition: An iHGS Database Acquisition Protocol

2022

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Background With the advances in current technology, hand gesture recognition has gained considerable attention. It has been extended to recognize more distinctive movements, such as a signature, in human-computer interaction (HCI) which enables the computer to identify a person in a non-contact acquisition environment. This application is known as in-air hand gesture signature recognition. To our knowledge, there are no publicly accessible databases and no detailed descriptions of the acquisitional protocol in this domain. Methods This paper aims to demonstrate the procedure for collecting the in-air hand gesture signature’s database. This database is disseminated as a reference database in the relevant field for evaluation purposes. The database is constructed from the signatures of 100 volunteer participants, who contributed their signatures in two different sessions. Each session provided 10 genuine samples enrolled using a Microsoft Kinect sensor camera to generate a genuine dataset. In addition, a forgery dataset was also collected by imitating the genuine samples. For evaluation, each sample was preprocessed with hand localization and predictive hand segmentation algorithms to extract the hand region. Then, several vector-based features were extracted. Results In this work, classification performance analysis and system robustness analysis were carried out. In the classification analysis, a multiclass Support Vector Machine (SVM) was employed to classify the samples and 97.43% accuracy was achieved; while the system robustness analysis demonstrated low error rates of 2.41% and 5.07% in random forgery and skilled forgery attacks, respectively. Conclusions These findings indicate that hand gesture signature is not only feasible for human classification, but its properties are also robust against forgery attacks.

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Filtering Data from Motion Sensors with Rich Features for Monitoring Brushing Behaviors

Abudalfa,

Bouchard

2024

Lecture Notes in Networks and Systems

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Toothbrushing data and analysis of its potential use in human activity recognition applications

Cited by 10 publications

References 15 publications

Monitoring Brushing behaviors using Toothbrush Embedded Motion-Sensors

Monitoring Brushing behaviors using Toothbrush Embedded Motion-Sensors

In-air Hand Gesture Signature Recognition: An iHGS Database Acquisition Protocol

Filtering Data from Motion Sensors with Rich Features for Monitoring Brushing Behaviors

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