Incremental Learning Techniques for Online Human Activity Recognition

• URL: http://arxiv.org/abs/2109.09435v1
• Date: Mon, 20 Sep 2021 11:33:09 GMT
• Title: Incremental Learning Techniques for Online Human Activity Recognition
• Authors: Meysam Vakili, Masoumeh Rezaei
• Abstract summary: We propose a human activity recognition (HAR) approach for the online prediction of physical movements. We develop a HAR system containing monitoring software and a mobile application that collects accelerometer and gyroscope data. Six incremental learning algorithms are employed and evaluated in this work and compared with several batch learning algorithms commonly used for developing offline HAR systems.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Unobtrusive and smart recognition of human activities using smartphones inertial sensors is an interesting topic in the field of artificial intelligence acquired tremendous popularity among researchers, especially in recent years. A considerable challenge that needs more attention is the real-time detection of physical activities, since for many real-world applications such as health monitoring and elderly care, it is required to recognize users' activities immediately to prevent severe damages to individuals' wellness. In this paper, we propose a human activity recognition (HAR) approach for the online prediction of physical movements, benefiting from the capabilities of incremental learning algorithms. We develop a HAR system containing monitoring software and a mobile application that collects accelerometer and gyroscope data and send them to a remote server via the Internet for classification and recognition operations. Six incremental learning algorithms are employed and evaluated in this work and compared with several batch learning algorithms commonly used for developing offline HAR systems. The Final results indicated that considering all performance evaluation metrics, Incremental K-Nearest Neighbors and Incremental Naive Bayesian outperformed other algorithms, exceeding a recognition accuracy of 95% in real-time.

Related papers

• Offline Imitation Learning Through Graph Search and Retrieval [57.57306578140857]
  Imitation learning is a powerful machine learning algorithm for a robot to acquire manipulation skills. We propose GSR, a simple yet effective algorithm that learns from suboptimal demonstrations through Graph Search and Retrieval. GSR can achieve a 10% to 30% higher success rate and over 30% higher proficiency compared to baselines.
  arXiv  Detail & Related papers  (2024-07-22T06:12:21Z)
• Know Thy Neighbors: A Graph Based Approach for Effective Sensor-Based Human Activity Recognition in Smart Homes [0.0]
  We propose a novel graph-guided neural network approach for Human Activity Recognition (HAR) in smart homes. We accomplish this by learning a more expressive graph structure representing the sensor network in a smart home. Our approach maps discrete input sensor measurements to a feature space through the application of attention mechanisms.
  arXiv  Detail & Related papers  (2023-11-16T02:43:13Z)
• Multi-Dimensional Ability Diagnosis for Machine Learning Algorithms [88.93372675846123]
  We propose a task-agnostic evaluation framework Camilla for evaluating machine learning algorithms. We use cognitive diagnosis assumptions and neural networks to learn the complex interactions among algorithms, samples and the skills of each sample. In our experiments, Camilla outperforms state-of-the-art baselines on the metric reliability, rank consistency and rank stability.
  arXiv  Detail & Related papers  (2023-07-14T03:15:56Z)
• Your Room is not Private: Gradient Inversion Attack on Reinforcement Learning [47.96266341738642]
  Privacy emerges as a pivotal concern within the realm of embodied AI, as the robot accesses substantial personal information. This paper proposes an attack on the value-based algorithm and the gradient-based algorithm, utilizing gradient inversion to reconstruct states, actions, and supervision signals.
  arXiv  Detail & Related papers  (2023-06-15T16:53:26Z)
• RMBench: Benchmarking Deep Reinforcement Learning for Robotic Manipulator Control [47.61691569074207]
  Reinforcement learning is applied to solve actual complex tasks from high-dimensional, sensory inputs. Recent progress benefits from deep learning for raw sensory signal representation. We present RMBench, the first benchmark for robotic manipulations.
  arXiv  Detail & Related papers  (2022-10-20T13:34:26Z)
• Classifying Human Activities using Machine Learning and Deep Learning Techniques [0.0]
  Human Activity Recognition (HAR) describes the machines ability to recognize human actions. Challenge in HAR is to overcome the difficulties of separating human activities based on the given data. Deep Learning techniques like Long Short-Term Memory (LSTM), Bi-Directional LS classifier, Recurrent Neural Network (RNN), and Gated Recurrent Unit (GRU) are trained. Experiment results proved that the Linear Support Vector in machine learning and Gated Recurrent Unit in Deep Learning provided better accuracy for human activity recognition.
  arXiv  Detail & Related papers  (2022-05-19T05:20:04Z)
• Classifying Human Activities with Inertial Sensors: A Machine Learning Approach [0.0]
  Human Activity Recognition (HAR) is an ongoing research topic. It has applications in medical support, sports, fitness, social networking, human-computer interfaces, senior care, entertainment, surveillance, and the list goes on. We examined and analyzed different Machine Learning and Deep Learning approaches for Human Activity Recognition using inertial sensor data of smartphones.
  arXiv  Detail & Related papers  (2021-11-09T08:17:33Z)
• Continual Learning in Sensor-based Human Activity Recognition: an Empirical Benchmark Analysis [4.686889458553123]
  Sensor-based human activity recognition (HAR) is a key enabler for many real-world applications in smart homes, personal healthcare, and urban planning. How can a HAR system autonomously learn new activities over a long period of time without being re-engineered from scratch? This problem is known as continual learning and has been particularly popular in the domain of computer vision.
  arXiv  Detail & Related papers  (2021-04-19T15:38:22Z)
• Physical Activity Recognition Based on a Parallel Approach for an Ensemble of Machine Learning and Deep Learning Classifiers [0.0]
  Human activity recognition (HAR) by wearable sensor devices embedded in the Internet of things (IOT) can play a significant role in remote health monitoring and emergency notification. This study investigates a human activity recognition method of accrued decision accuracy and speed of execution to be applicable in healthcare.
  arXiv  Detail & Related papers  (2021-03-02T16:50:52Z)
• Continuous Emotion Recognition via Deep Convolutional Autoencoder and Support Vector Regressor [70.2226417364135]
  It is crucial that the machine should be able to recognize the emotional state of the user with high accuracy. Deep neural networks have been used with great success in recognizing emotions. We present a new model for continuous emotion recognition based on facial expression recognition.
  arXiv  Detail & Related papers  (2020-01-31T17:47:16Z)
• Adversarial vs behavioural-based defensive AI with joint, continual and active learning: automated evaluation of robustness to deception, poisoning and concept drift [62.997667081978825]
  Recent advancements in Artificial Intelligence (AI) have brought new capabilities to behavioural analysis (UEBA) for cyber-security. In this paper, we present a solution to effectively mitigate this attack by improving the detection process and efficiently leveraging human expertise.
  arXiv  Detail & Related papers  (2020-01-13T13:54:36Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.