Advancing Healthcare and Elderly Activity Recognition: Active Machine and Deep Learning for FineGrained Heterogeneity Activity Recognition
ABSTARCT :
This research explores the potential of technologies in human activity recognition among the elderly population. More precisely, using sensor data and implementing Active Learning (AL), Machine Learning (ML), and Deep learning (DL) techniques for elderly activity recognition.
Moreover, the study leverages the HAR70+ dataset, providing insight into the daily activities of older individuals and AL-based ML and DL techniques to construct predictive models for these activities.
The findings have implications for proactive and personalized elderly care, representing an approach to improving prediction performance in this domain. The research experiments are presented systematically, summarizing the outcomes of various machine-learning models across three iterative experiments.
This research explored a diverse array of ML algorithms, including Random Forest (RF), Extreme Gradient Boosting (XGBoost), Logistic Regression (LR), K-Nearest Neighbors (KNN), Stochastic Gradient Descent (SGB) and DL methods such as Deep Neural Networks (DNN) and Long Short-Term Memory networks (LSTM) for experimentation.
This research trained models on 7 activities: walking, shuffling, climbing stairs (up and down), standing, sitting, and lying down, and 4 activities separately: standing, sitting, walking, and lying down, using the same classifiers.
Results reveal that LSTM achieved the best accuracy of 0.95% for 7 activities and 0.96% using RF on 4 actives, showing the potential of DL and ML techniques, particularly when integrated with AL, to enhance activity recognition rate, patient care, optimize medication strategies and improve the well-being of elderly individuals.
Hence, the findings presented in this study have showcased the potential to enhance the quality of life for seniors using the blend of ML, DL and AL.
EXISTING SYSTEM :
These systems typically combine various sensors, machine learning algorithms, and deep learning models to monitor elderly individuals' activities, identify potential health risks, and improve the overall quality of care.
One such system is activity recognition frameworks based on wearable devices like smartwatches, accelerometers, and gyroscopes, which are commonly used in healthcare settings.
These devices continuously collect motion data, which is then processed to recognize a range of activities such as walking, sitting, standing, and even more complex tasks like climbing stairs or bending down.
Deep learning models, particularly Recurrent Neural Networks (RNNs), Long Short-Term Memory (LSTM) networks, and Convolutional Neural Networks (CNNs), have been employed to analyze these sensor data streams.
DISADVANTAGE :
Sensitive Data: Healthcare and elderly activity recognition often involves sensitive data, including physical activity, health conditions, and sometimes personal information. Ensuring data privacy and securing this information is a major concern, especially with the use of cloud computing and third-party services.
Resource Intensity: Deep learning models, particularly those used for fine-grained recognition tasks, require significant computational resources for training and inference.
System Integration: Deploying complex deep learning models in real-world healthcare environments is difficult due to the need for integration with existing systems, healthcare devices, and sensors. This integration can lead to compatibility issues and high operational costs.
Imbalanced Data: Elderly activity datasets often suffer from class imbalances, where certain activities may be underrepresented. This leads to model bias and reduced accuracy for less common activities.
PROPOSED SYSTEM :
The steps of our Proposed approach are described in this part, including dataset Information, features and the machine learning algorithm using Active Learning approaches.
bilities are used to measure this uncertainty, allowing the model to pinpoint situations where it is least confident in its predictions. The active learning procedure ensures that the model focuses on the trickiest cases by repeatedly identifying these uncertain samples, boosting overall accuracy.
The uncertain sample expands the training set and is added to the labeled dataset. On this revised dataset, the model is retrained, improving its comprehension of the intricacy of the data.
The chosen sample is eliminated from the test set to prevent repetition in subsequent iterations. After training, the model’s predictions are contrasted with the actual labels of the remaining test samples.
ADVANTAGE :
Fine-Grained Recognition: Deep learning models are highly effective at recognizing and distinguishing between subtle variations in activities. For elderly care, this means the system can accurately detect a wide range of activities, from simple movements like walking to more complex actions like sitting down or picking up objects.
Dynamic Learning: As elderly individuals may undergo changes in physical abilities or health conditions over time, active learning can enable systems to update and refine their models based on new data, ensuring that recognition remains accurate and up-to-date.
Automatic Activity Logging: With accurate activity recognition, there is no need for manual logging or tracking of elderly individuals’ movements. This reduces the burden on caregivers and healthcare providers, allowing them to focus on more critical tasks.
Medication Adherence: Activity recognition systems can be integrated with reminders for medication adherence, helping elderly individuals stay on track with their prescribed health regimens, which can improve overall health outcomes.
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