Human Activity Recognition Using Machine Learning Projects

Human Activity Recognition (HAR) is a domain of research in which its aim is to find the movement performed by a person depending on particular inputs, generally accelerometer and gyroscope data from smart mobiles and wearable devices. Get your problem statement done from verified professionals at we provide innovative solutions for all your research work.  HAR has applications in different domains like tracking health, gaming and supervising. Share with us all your research issues we will provide you with best results.

Here are the processing steps which we use for a basic HAR project using ML:

  1. Data Collection:
  • Smartphone Sensors: We gather data from accelerometer and gyroscope sensors from famous datasets like UCI HAR dataset.
  • Video: To utilize videos we deploy other methods like optical flow.
  1. Pre-processing the Data:
  • Noise Filtering: For resolving the data we utilize a low-pass filter because raw sensor data always involves errors.
  • Segmentation & Windowing: We split the consistent stream of data into constant size windows such as a 10-second window with 50% overlap.
  • Feature Extraction: Retrieving statistical (mean, variance, etc.) and frequency-based (FFT coefficients) features from every window.
  1. Framework Selection:

To select an applicable ML framework we implement some famous options for HAR like:

  • Decision Trees
  • Random Forests
  • Support Vector Machines (SVM)
  • Neural Networks such as LSTM and Convolutional Neural Networks (CNNs) assist us.
  1. Training the Model:
  • We divide the data into training and validation sets.
  • Our system is trained on the training set.
  1. Model Evaluation:
  • On the validation set we check the framework’s efficiency.
  • Accuracy, precision, F1-score and recall are the general metrics we employ in our model.
  1. Deployment:

When we get satisfaction from our model we apply it in real-time applications like:

  • Privacy: By detecting malicious behaviors we ensure security.
  • Healthcare: We track patient’s events and predicting abnormalities.
  • Sports: To observe a player’s performance we incorporate our model.
  1. Extensions:
  • Transfer Learning: Implementing a pre-trained model and we adjust it for HAR.
  • Multi-modal Recognition: For increasing the accuracy we integrate data from several sensors and sources.
  • Real-time Analyzing: We incorporate our model in real-world situations.

Tools & Libraries:

  • Data Gathering & Pre-processing: Python with libraries such as Pandas and NumPy is helpful in our project.
  • ML: Scikit-learn, TensorFlow and Keras are common options which support us.


  • It is important for us to make sure that the data is well-labeled and defines multiple situations. Because the quality of the data is challenging.
  • For deep learning methods we utilize a huge dataset to increase efficiency.
  • We practice with various structures and hyperparameters.
  • By constantly retraining our model with fresh data it serves us in enhancing and handling the efficiency of the model.

We conclude that it is important to keep us updated with recent research in this area. The latest techniques and developments are consistently built in the field of HAR.

Human Activity Recognition Using Machine Learning Topics

Human Activity Recognition Using Machine Learning Thesis Ideas

The recent thesis ideas that we have carried on Human Activity Recognition Using Machine Learning are listed below, go through our work and contact us for more support.

  1. Integrating Multiple Public Datasets for Human Activity Recognition using Machine Learning


Human Activity Recognition, Data Integration, Machine Learning

We suggested a combined dataset in our article for Human Activity Recognition (HAR) that is utilized to train and evaluate various ML approaches such as J48, KNN, LR, MLP, NB, RF, and SVM. These approaches are examined with regards to various performance metrics to compare the efficiency. As a consequence, Random Forest provides greater end results than other methods in recognition of human activity.

  1. Evaluation of a Combined Conductive Fabric-Based Suspender System and Machine Learning Approach for Human Activity Recognition


Conductive fabric sensor, Deep learning, E-textile, Human activity, Smart textile, Wearable.

An enhanced body worn suspender related HAR model is recommended in our paper that is constructed by utilizing a conductive knit jersey fabric material that addresses the issues in previous strain sensor related wearable device and also it offers enhanced sensitivity. We employed ML and DL methods to detect various human activities. We compared the ML related dimensionality reduction methods, in that, kernel method offers best results than linear method.

  1. Human Activity Recognition Using Machine Learning Algorithms Based on IMU Data


Inertial Measurement Unit (IMU), ANN, Random Forest, Decision Tree, KNN

A main aim of our paper is to recognize several human activities including walking upstairs and downstairs, walking on the toe or on heel, walking ordinarily etc. We carried out this recognition process by using ML methods like ANN, DTC, RFC, and KNN. Our research is considered as a significant step that helps to increase the utility and efficacy of wearable robotics and enhance the quality of disabled person’s life. In that, RFC outperforms other methods.

  1. Investigation of Machine Learning Models for Human Activity Recognition: A Comparative Study


Human Activity Detection, Artificial Intelligence, Performance Evaluation, Ambient Assisted Living

A practical execution of various ML based approaches for human activity detection is the main concentration of our research. Features are chosen based on tree-based classifiers are exists in the dataset that are utilized to evaluate the efficiency of various methods like Long Short Term Memory, Decision Tree, Naive Bayes etc. Results show that, LSTM achieved highest performance than other methods.

  1. Human activity recognition in cyber-physical systems using optimized machine learning techniques


Optimization, Stochastic gradient descent, Convolutional neural networks (CNN), Long short term memory (LSTM)

To recognize the human activity, various ML techniques such as RF, DT, k-NN and several DL techniques like CNN, LSTM, and GRU are employed in our article. We utilized optimization methods along with DL techniques to enhance our framework’s efficiency. We used SGD and optimizers Adam and RMSProp and examined the performance of this framework. We states that, our suggested framework provides better outcomes.

  1. A Comprehensive Comparison of Machine Learning Approaches with Hyper-Parameter Tuning For Smartphone Sensor-Based Human Activity Recognition


ADL, SVM, Ensemble Learning, Gradient Boosting, GridSearch

A comparative analysis of several ML methodologies with hyper parameter adjustment on dataset is recommended in our study to categorize the human activities. We conclude that, in the human activity categorization process by utilizing GridSearch for hyper parameter adjustment, SVM and Ensemble learning methods including RF and Gradient Boosting achieved greater efficiency. Our suggested model offers highest end results than other previous models.

  1. Machine Learning (ML) based Human Activity Recognition Model using Smart Sensors in IoT Environment


Internet of Things (IoT), Smart Phone and Sensors

An IoT related tracking of human activities are suggested in our research to frequently monitor the movements of elder people through the use of smart sensor related techniques. We gathered the data through IoT related devices or smart sensors and the datas are examined by various ML methods to identify the chances of risks in the behaviour of elder people. As a result, SVM is considered as an efficient method in the recognition process than other approaches.

  1. Human Activity Recognition Using Machine Learning Technique


Monitoring, estimating, measuring, EEG signal, muse detector

We developed an approach to evaluate the capacity of cognitive brain power of students while attending virtual classes by utilizing EEG datas collected through muse headset. We preprocessed the gathered multi-dimensional data and input the data into ML techniques. We selected the relevant features to minimize the size of the dataset. Our framework is evaluated in terms of various performance metrics and it is stated that, SVM achieved better efficiency.

  1. Machine Learning-Based Human Activity Recognition Using Smartphones


Healthcare and sensor fusion

An efficient ML based method is developed in our article to examine the data that are gathered from smartphones sensors such as accelerometer and 3D gyroscope. To reduce the size of the dataset is the major aim of this article. We utilized the combination of ML based supervised and active learning methods including LR, QDA, KNN, SVM, ANN, DT and NB to categorize and forecast the human activities and our suggested method offers better end results.

  1. Human Activity Recognition for Elderly People Using Machine and Deep Learning Approaches


Elderly people

To assist the aged people in all circumstances by tracking their activities through gyroscope and accelerometer data gathered from a smart phone is the main concentration of our approach. We utilized monitored datas related to daily activities of aged or disabled persons. For recognizing human activities, we employed ML and DL methods like KNN, RF, SVM, ANN and LSTM Network. In that, SVM categorization method provides greater outcomes.