Human detection and tracking system are one of the most fundamental tasks in computer vision field where motion detection and tracking are coupled to locate human through the video captured by an observer (installed camera). Optical flow algorithm is one of the methods that used to analyze the path of pixels and reflects and the apparent changes of the targeted moving object between previous and current locations. It is frequently applied in human detection and tracking system since it is easier to be applied to input sources and variations-effective. However, detecting humans in sequences of frames or even in a real-time video is still very challenging due to their appearance variations and different poses adoptions.

Security is becoming the main concern of society in urban cities since the scarcity of jobs raise, which indicates that property of residents is being threatened by thefts and destruction. Traditional surveillance systems need human operators to differentiate all activities and behaviors of objects for forensic investigation purpose and require significant judgment and decision for an event happening, there is a probability of mistake due to a long period of passive watching which results in negligence. Besides, a huge amount of data received is impossible to be handled and extracted due to time-consuming and high expenses. Therefore, this project is proposed to develop a computer vision system for real-time human detection and tracking using optical flow algorithm.

Human will be identified and tracked with classifier with the features extracted by the human descriptor. Human behaviors will be analyzed and interpreted based on video frames obtained from the observer (camera). Raspberry Pi 3 board is selected to develop and implement detection and tracking and FAVORIOT middleware will be used as a communication media that allows all data from IoT end device to be uploaded and used as administration references.

[Note: This project is being done by UPM, our FAVORIOT’s University’s collaborator]

You can check out the whole LIST of IOT PROJECTS by our University Collaborators.

Nowadays, there has been a high exposure to ethanol that can give side effect to the human. The cost-effective on monitoring the ethanol concentration with high selectivity based on optical based is developed. The ethanol sensor is developed using uncladded plastic optical fiber (UCPOF) and being coated with nanomaterials. The system is using the photodiode to interrogate the signal since it has the high sensitivity on wavelengths changes. The light source that travels along the ethanol sensor will be converted into the corresponding current. Then, the trans-impedance amplifier will convert the current into a voltage and received by Arduino UNO microcontroller. The sensor is having the ability to monitoring the remote sensor since it developed in small size. The advancement of this system, the project can monitor the ethanol in the long distance with the aid of Internet of Things (IoT) by using FAVORIOT IoT Cloud. The power of light intensity will be recorded in the Cloud. The collection of data in this system can be viewed and monitored in FAVORIOT website. The recorded data can be used to taking the fast action when will have the high exposure to ethanol.

[Note: This project is being done by UPM, our FAVORIOT’s University’s collaborator]

You can check out the whole LIST of IOT PROJECTS by our University Collaborators.

It’s no doubt that the whole world is facing an Ageing population. In 2017, there are an estimated 962 M people aged 60 or over in the world, comprising 13 % of the global population. The population aged 60 or above is growing at a rate of about 3 % per year. With the progression in healthcare, people tend to live longer than before, however, not necessarily healthier. They might suffer several chronic diseases such as Diabetes, high blood pressure or Dementia.

It was stated 500K elderly people gone missing in China every year or 1300 elderly people went missing every day due to loss of memory. Senior citizens aged 65 or over account for up to 80% of missing elderly person cases (Alzheimer or dementia or memory impairment). Of those who were found after being reported missing, 25% then went missing again.

Statistics have shown that approximately 30% of people over 65 falls accidentally each year, and for those over 80 this rate raises up to 50%. Late or non-existing responses cause 2 million persons having to be hospitalized and approximately 85K deaths can be attributed each year. Among older persons, 55 % of fall injuries occur inside the home. An additional 23 % occur outside, but near their homes.

Thus, there are three key things that we consider very seriously for the elderly well-being (i) continuous monitoring of their vital health conditions (ii) monitoring their location in case they get lost (iii) emergency alerts during falls.

The trend is moving towards “Aging in Place” whereby we are seeing the elderly are staying alone especially in countries where they have less number of children or the culture whereby the children or the parents prefer to stay on their own. As a working adult, they have no options but to let their parents stayed at home by themselves. In such case, it will bring high anxiety especially when their parents suffer some disease or in a frail condition.

How do we let the elderly to “Live Alone but Not Left Alone”? What if we have a way to monitor their health continuously? What if we know they are always active or just lie on the bed? What if we get notified whenever they walked outside their house? What if they need emergency and need to call their next of kin or neighbor easily? What if you constantly call them directly without them carrying a mobile phone?

All of the above is possible using our Favorwatch. Check out the details here.

Anyone willing to join the mission to connect and help 1 Million elderly around the world?