inzicht - Ubiquitous Computing - # Annotation Methods Comparison in Human Activity Recognition

An Empirical Study on In Situ and Self-Recall Activity Annotations from Wearable Sensors

Q: How can researchers ensure high-quality annotations in real-world datasets without burdening participants

To ensure high-quality annotations in real-world datasets without burdening participants, researchers can implement a combination of strategies. One approach is to provide participants with user-friendly tools and interfaces that make the annotation process easier and more intuitive. This could include interactive visualization tools that allow participants to inspect their data visually and set labels accordingly. By providing clear instructions and guidance on how to use these tools effectively, researchers can help participants accurately annotate their activities without feeling overwhelmed. Another strategy is to incorporate feedback mechanisms into the annotation process. For example, using on-device prompts or reminders can help remind participants to annotate their activities at specific times throughout the day. Additionally, incorporating gamification elements or incentives for accurate annotations can motivate participants to engage more actively in the annotation process. Furthermore, researchers should consider the preferences and capabilities of their study participants when designing annotation methodologies. Providing flexibility in how annotations are made (e.g., allowing for handwritten diaries or app-assisted methods) can cater to different participant needs and preferences, ultimately leading to higher quality annotations. By combining user-friendly tools, feedback mechanisms, incentives for accuracy, and flexibility in annotation methods, researchers can ensure high-quality annotations in real-world datasets while minimizing the burden on participants.

Q: What are the implications of using different annotation methodologies on the generalizability of deep learning models

The implications of using different annotation methodologies on the generalizability of deep learning models are significant. The choice of annotation method directly impacts the quality and consistency of annotations, which in turn affects the performance of trained deep learning models. For instance: In situ methods may result in more precise but fewer labels compared to recall methods. Self-recall methods like writing an activity diary may lead to imprecise time indications. Combining self-recall with visualization tools can improve missing annotations and increase consistency. These differences in annotation quality have a direct impact on deep learning model capabilities. Models trained on datasets with accurate and consistent annotations are likely to perform better than those trained on noisy or incorrect data. In terms of generalizability: Models trained with high-quality annotated data are more likely to generalize well across different scenarios. Variations in labeling methodologies may introduce biases that affect model performance across diverse datasets. Ensuring consistent labeling practices across datasets is crucial for building robust models that can generalize effectively beyond specific study contexts.

Q: How can machine learning techniques address noisy or incorrect annotations in sensor-based human activity recognition datasets

Machine learning techniques offer several approaches for addressing noisy or incorrect annotations in sensor-based human activity recognition datasets: Bootstrapping: Initially training a model with a small subset of high-confidence labels before iteratively improving it by incorporating additional data helps mitigate errors propagated from wrong-labeled samples. Loss Functions: Using specialized loss functions designed specifically for handling noisy labels allows models to learn from incorrectly labeled instances without being overly influenced by them during training. Active Learning: Incorporating active learning strategies where models interactively query users for clarification on ambiguous instances helps refine predictions over time based on user feedback. Semi-Supervised Learning: Leveraging both labeled and unlabeled data through semi-supervised learning algorithms enables models to learn from partially annotated datasets while reducing reliance on potentially erroneous labels. By implementing these machine learning techniques tailored towards handling noisy or incorrect annotations, researchers can improve model robustness and accuracy when working with sensor-based human activity recognition datasets containing imperfectly labeled data points.

Belangrijkste concepten

Different labeling methodologies impact annotation quality and deep learning model performance in activity recognition studies.

Samenvatting

An empirical study compared 4 annotation methods for in-the-wild data, showing in situ methods produce more precise labels than recall methods. Combining an activity diary with a visualization tool improved annotation consistency and deep learning model F1-Score by up to 8%. Sensor-based activity recognition requires multimodal inputs for complex activities. Long-term data recording is challenging due to contextual differences between controlled and uncontrolled environments. Annotation biases like self-recall bias can affect classifier performance. Deep learning analysis showed the impact of annotation quality on classification results.

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Annotations' F1-Score improved by up to 8% with combined diary and visualization tool.
Participants wore Bangle.js smartwatch for 2 weeks.
In situ methods produced less but more precise labels than recall methods.
Average missing annotations: Week 1 - In situ button (40%), In situ app (43.4%), Pure self-recall (4.3%). Week 2 - In situ button (49.05%), In situ app (56.79%), Pure self-recall (8.14%).
Cohen κ scores varied across annotation methods.

Citaten

"Annotations' quality impacts deep learning model capabilities directly."
"In situ methods produce less but more precise labels than recall methods."
"Combining an activity diary with a visualization tool improved annotation consistency."

Belangrijkste Inzichten Gedestilleerd Uit

A Matter of Annotation

by Alexander Ho... om arxiv.org 03-18-2024

https://arxiv.org/pdf/2305.08752.pdf

Diepere vragen

How can researchers ensure high-quality annotations in real-world datasets without burdening participants

To ensure high-quality annotations in real-world datasets without burdening participants, researchers can implement a combination of strategies. One approach is to provide participants with user-friendly tools and interfaces that make the annotation process easier and more intuitive. This could include interactive visualization tools that allow participants to inspect their data visually and set labels accordingly. By providing clear instructions and guidance on how to use these tools effectively, researchers can help participants accurately annotate their activities without feeling overwhelmed.
Another strategy is to incorporate feedback mechanisms into the annotation process. For example, using on-device prompts or reminders can help remind participants to annotate their activities at specific times throughout the day. Additionally, incorporating gamification elements or incentives for accurate annotations can motivate participants to engage more actively in the annotation process.
Furthermore, researchers should consider the preferences and capabilities of their study participants when designing annotation methodologies. Providing flexibility in how annotations are made (e.g., allowing for handwritten diaries or app-assisted methods) can cater to different participant needs and preferences, ultimately leading to higher quality annotations.
By combining user-friendly tools, feedback mechanisms, incentives for accuracy, and flexibility in annotation methods, researchers can ensure high-quality annotations in real-world datasets while minimizing the burden on participants.

What are the implications of using different annotation methodologies on the generalizability of deep learning models

The implications of using different annotation methodologies on the generalizability of deep learning models are significant. The choice of annotation method directly impacts the quality and consistency of annotations, which in turn affects the performance of trained deep learning models.
For instance:

In situ methods may result in more precise but fewer labels compared to recall methods.
Self-recall methods like writing an activity diary may lead to imprecise time indications.
Combining self-recall with visualization tools can improve missing annotations and increase consistency.
These differences in annotation quality have a direct impact on deep learning model capabilities. Models trained on datasets with accurate and consistent annotations are likely to perform better than those trained on noisy or incorrect data.
In terms of generalizability:

Models trained with high-quality annotated data are more likely to generalize well across different scenarios.
Variations in labeling methodologies may introduce biases that affect model performance across diverse datasets.
Ensuring consistent labeling practices across datasets is crucial for building robust models that can generalize effectively beyond specific study contexts.

How can machine learning techniques address noisy or incorrect annotations in sensor-based human activity recognition datasets

Machine learning techniques offer several approaches for addressing noisy or incorrect annotations in sensor-based human activity recognition datasets:

Bootstrapping: Initially training a model with a small subset of high-confidence labels before iteratively improving it by incorporating additional data helps mitigate errors propagated from wrong-labeled samples.

Loss Functions: Using specialized loss functions designed specifically for handling noisy labels allows models to learn from incorrectly labeled instances without being overly influenced by them during training.

Active Learning: Incorporating active learning strategies where models interactively query users for clarification on ambiguous instances helps refine predictions over time based on user feedback.

Semi-Supervised Learning: Leveraging both labeled and unlabeled data through semi-supervised learning algorithms enables models to learn from partially annotated datasets while reducing reliance on potentially erroneous labels.

By implementing these machine learning techniques tailored towards handling noisy or incorrect annotations, researchers can improve model robustness and accuracy when working with sensor-based human activity recognition datasets containing imperfectly labeled data points.