Statistics show that the population of aging people is increasing and in the near future, human assistance for elderly people will be expensive because the ratio of caretakers is less than the ratio of the elderly population. Hence, we have proposed a system that will be able to monitor the act
Elderly Monitoring Application Through Human Activity Detection
Statistics show that the population of aging people is increasing and in the near future, human assistance for elderly people will be expensive because the ratio of caretakers is less than the ratio of the elderly population. Hence, we have proposed a system that will be able to monitor the activity of the elderly through mobile motion sensors. This project is concerned with identifying the specific movement or action of a person based on sensor data by its classification into one of the training classes. The system will be able to intelligently detect when an older adult is passive or moving e.g., take a walk, sit, fall, etc.
The application will provide two modules, one for the patient and the other for the caregiver. When anomalous activity is detected the phone will notify the caregiver with a notification over his device. Multiple sensor available in mobile phones such as accelerometer, gyroscope, gravity sensor etc can be used for the proposed system. MobiFall dataset will be used in the prototype for testing of the system.
The main objective of that the project will achieve are:
The project shall involve the following steps for implementation:
First of all, we shall work on the logic layer and collect the data input from the mobile phone. Than we load the dataset into a python based model. Dataset is the collection of data pieces that can be treated by computer as single unit for analytic and prediction purpose. To do preliminary processing of collected data we preprocess it. After that we train the dataset through machine learning algorithms. For supervise machine learning we use labelled dataset, so that machine can easily and clearly understand the input patterns than we train the modules and test them. After all the process we made the final model so through that we demonstrate the results.

Our proposed system detects dangerous activities such as falls to provide necessary help in time.
The final deliverable will be an androud application with an integrated AI module.
Android application:
Artificial Intelligence Module:
| Item Name | Type | No. of Units | Per Unit Cost (in Rs) | Total (in Rs) |
|---|---|---|---|---|
| GeForce GTX 1070 | Equipment | 1 | 65000 | 65000 |
| Total in (Rs) | 65000 |
Accurate detection and counting of persons are essential in natural disaster management an...
This was observed that fake pesticides and fertilizers have a negative effect, and they po...
?Households of today are becoming smarter and more automated. Home automation delivers con...
Smart Plants Managment Information System(SPMIS):- The goal of this project was to create...
In modern day computations, especially where analog sensors are interfaced with the comput...