School of Science and Technology 科技學院
Computing Programmes 電腦學系
| Programme | Bachelor of Science with Honours in Web Technologies |
| Supervisor | Dr Jeff Tang |
| Areas | Innovative Systems |
| Year of Completion | 2015 |
This project is aiming to provide an integrated, smart platform that can able to study the behavior of elderly residents. We decided to develop an Android application with Elderly safety alert system. The final product we expected the system could determine whether the elderly fell down or not and notify the guardian when the elderly need assistance.
The concept of our system is to provide a promising and cost-effective way of improving safety alert for the elderly residents and the disabled in a non-obtrusive way. Therefore we will implement a personal safety system that keeps an eye on elderly residents, monitoring their behaviors and serves as an alert system to notify users if they are injured or incapacitated. A brief line of message will be send to the guardian’s station or smart device which indicates the activity or abnormal incident about the resident. Guardian could also monitor from their monitor to acknowledge the condition from the web camera of the system that place in the location. For the rest of the project proposes a handy, simplicity approach to achieve home care, react and response to variable tasks performed both end user and elderly residents.
The whole system can be divided to three components, which is the Data Collection Side, Server Side and Message Receiver Side.
Here using three kind of devices, which is Android wear, Android phone/tablet and webcam or Kinect. Android Wear Easier for elderly to accept modern technologies, as they usually think it requires knowledge of controlling any smart devices. Therefore, Wrist watches are the perfect and easiest approach to collect the data application need and act as a safety alert system to the elderly. To ease the difficulty in hardware production, we decided to develop on the Android Wear manufactured by Motorola during the project.
Android wear (Moto 360) which is an Android powered wearable device and required to connecting with an android phone via Bluetooth. According to the data from Motorola the specification are listed below:
Moto 360 is assumed wearing on the elderly left wrist, while elderly got movement; our application will start to collect motion data from accelerometer and doing motion analysis at the same time.
Android Phone/Tablet
Currently most android wear does not have internet connection ability, required to connect an android device as the bridge of the internet connection. Also, the system need to response to the server at real time. Due to this problem, we use the android mobile device to connect the TCP Socket Server for returning the Elderly status in JSON format to the Message Receiver Side via the mobile network or Wi-Fi network.
Webcam / Kinect
Since the system will include the feature of real time home view for the care taker in the Message Receiver Side. When the care taker received the elderly fallen notification, then he/she can take a look on the elderly real time image to check the elderly current condition. Currently, we use Kinect as a webcam only. In the future, the Kinect may consider as the device that for motion detection. The situation of the connection between PC and webcam are illustrated at middle of the figure xx that surrounding with red and yellow rectangle.
PC (Server Side)
PC is used for connecting the Kinect represent as a bridge of the TCP Socket connection to returning the real time image in JSON format to the Message Receiver Side via the mobile network or Wi-Fi network.
Main Server
The main server is reserved for receiving the data from Data Collection and Message Receiver and return the suitable data to Message Receiver Side, which is used for push notification for the Message Receiver Side built by the TCP Socket Server with Multithreading that can accept multiple connection written in python. After receiving the data from Data Collection Side, the server will check the data a consistent or not and do decision making, return the data to the right algorithms.
The application collects data from elderly's daily activities to detect their fall signal. Since we required to develop the application for collects data from elderly's daily activities to detect their fall signal, we have chosen Moto 360 as our data input source. Moto360 is running on Android wear operation system, which provides different kind of API for developer to develop third party application for wearable devices.
The following table summarizes the motion sensors that are available on the Android platform [2] For the case of our application, we only use the hardware-based sensor, Accelerometer to apply on our application. (The sensor that highlighted in green in following table)
The classifier requires input motion data on every second, it consumes the battery quite a lot. As most of the Android Wearable device does not come with large capacity of battery, the application consumes around 1% of battery in every 2 minutes. Moreover, it is not feasible to slow down the application to decelerate the use of battery, as the application could not analysis the motion data in real-time manner. After a few trial and error and look up in API manual, we discovered that there were 4 kinds of sampling rate in SensorManager section, the related information are listed below:
To detect a short period of fall signal, a multilayer perceptron neural network is a commonly used ANN structure was selected as the most powerful classification algorithm for precise classification. The input layer consists of 28 neurons which represent the number of features that have been extracted on each sliding window. The proposed neural network was trained for 20 times using back-propagation algorithm as the learning technique. The training process is finding the optimal weight between each neuron.
The classifier multiply with each input feature with the weight trained by back-propagation through the hidden layer, and determine whether the output result is below or above the threshold. The output of the neural network has 3 possible states, “Fall”, “Normal” and “Potential Fall”.
To minimize the situation of outputting “Potential Fall”, Dynamic Time Warping (DTW) is implemented to recognize a motion before the neural network detects a fall signal. The purpose of DTW was to minimize the occurrence of “Potential Fall” situation, while DTW could recognize whether the elderly is performing a certain activity. Therefore, the result of the classifier will be more promising.
The alignment of measurements by DTW for measuring the distance between two sequences and the DTW obtains a mapping between the sequences. The black squares denote the optimum warping path.
| Neural Network | Neural Net + DTW | |||
|---|---|---|---|---|
| TRUE | FALSE | TRUE | FALSE | |
| Case 1 Positive | 10 | 3 | 10 | 3 |
| Negative | 7 | 0 | 7 | 0 |
| Case 2 Positive | 10 | 0 | 10 | 0 |
| Negative | 10 | 0 | 10 | 0 |
| Case 3 Positive | 10 | 2 | 10 | 3 |
| Negative | 8 | 0 | 7 | 0 |
| Case 4 Positive | 6 | 1 | 6 | 0 |
| Negative | 9 | 4 | 10 | 4 |
| Case 5 Positive | 5 | 2 | 7 | 2 |
| Negative | 8 | 5 | 8 | 3 |
| Neural Network | Neural Net + DTW | |||
|---|---|---|---|---|
| Type I error | Type II error | Type I error | Type II error | |
| Case 1 | 15% | 0% | 15% | 0% |
| Case 2 | 0% | 0% | 0% | 0% |
| Case 3 | 10% | 0% | 15% | 0% |
| Case 4 | 5% | 20% | 0% | 20% |
| Case 5 | 10% | 25% | 10% | 15% |
| Mean error rate | 17% | 15% | ||
| Total error rate | 8.50% | 7.50% |
Overall the classifier is quite successful, since it fulfills the needs of detecting the elderly fall signal using their motion data. In most of the case, it could able to alert users when there is fall pattern in the motion data. However, there are still have space to improve a lot. The further explanation will describes improvement in the later section.
Most users satisfied with the design of the application, especially the simplicity of the application, it could easily to carry and operate with both elderly and family members. Also, most of them agreed that the application did not requires a lot of extra equipment to achieve the benefits of ensuring elderly's safety.
Although the application meets the requirement of users, there are still have few drawbacks can be improved. First, DTW process can detect more different activity by increasing the window size. Currently the DTW process only input fixed number of data-points as a single input, which limit the variety of motion, as some movement may need larger window size to stores. Moreover, adding more templates to increase the accuracy of motion detection. As the template amount increases, the DTW process could handle more motion data from person with different characteristics such as walking pattern, body joint movement.
Copyright Wong Yiu Hong, Li Kin Lun, Poon Ka Ho and Jeff Tang 2015
Jonathan handles all external affairs include business development, patents write up and public relations. He is frequently interviewed by media and is considered a pioneer in 3D printing products.
After graduating from OUHK, Krutz obtained an M.Sc. in Engineering Management from CityU. He is now completing his second master degree, M.Sc. in Biomedical Engineering, at CUHK. Krutz has a wide range of working experience. He has been with Siemens, VTech, and PCCW.
Hugo Leung Wai-yin, who graduated from his four-year programme in 2015, won the Best Paper Award for his ‘intelligent pill-dispenser’ design at the Institute of Electrical and Electronics Engineering’s International Conference on Consumer Electronics – China 2015.
The pill-dispenser alerts patients via sound and LED flashes to pre-set dosage and time intervals. Unlike units currently on the market, Hugo’s design connects to any mobile phone globally. In explaining how it works, he said: ‘There are three layers in the portable pillbox. The lowest level is a controller with various devices which can be connected to mobile phones in remote locations. Patients are alerted by a sound alarm and flashes. Should they fail to follow their prescribed regime, data can be sent via SMS to relatives and friends for follow up.’ The pill-dispenser has four medicine slots, plus a back-up with a LED alert, topped by a 500ml water bottle. It took Hugo three months of research and coding to complete his design, but he feels it was worth all his time and effort.
Hugo’s public examination results were disappointing and he was at a loss about his future before enrolling at the OUHK, which he now realizes was a major turning point in his life. He is grateful for the OUHK’s learning environment, its industry links and the positive guidance and encouragement from his teachers. The University is now exploring the commercial potential of his design with a pharmaceutical company. He hopes that this will benefit the elderly and chronically ill, as well as the society at large.
Soon after completing his studies, Hugo joined an automation technology company as an assistant engineer. He is responsible for the design and development of automation devices. The target is to minimize human labor and increase the quality of products. He is developing products which are used in various sections, including healthcare, manufacturing and consumer electronics.
| Course Code | Title | Credits | |
|---|---|---|---|
| COMP S321F | Advanced Database and Data Warehousing | 5 | |
| COMP S333F | Advanced Programming and AI Algorithms | 5 | |
| COMP S351F | Software Project Management | 5 | |
| COMP S362F | Concurrent and Network Programming | 5 | |
| COMP S363F | Distributed Systems and Parallel Computing | 5 | |
| COMP S382F | Data Mining and Analytics | 5 | |
| COMP S390F | Creative Programming for Games | 5 | |
| COMP S492F | Machine Learning | 5 | |
| ELEC S305F | Computer Networking | 5 | |
| ELEC S348F | IOT Security | 5 | |
| ELEC S371F | Digital Forensics | 5 | |
| ELEC S431F | Blockchain Technologies | 5 | |
| ELEC S425F | Computer and Network Security | 5 |
| Course Code | Title | Credits | |
|---|---|---|---|
| ELEC S201F | Basic Electronics | 5 | |
| IT S290F | Human Computer Interaction & User Experience Design | 5 | |
| STAT S251F | Statistical Data Analysis | 5 |
| Course Code | Title | Credits | |
|---|---|---|---|
| COMPS333F | Advanced Programming and AI Algorithms | 5 | |
| COMPS362F | Concurrent and Network Programming | 5 | |
| COMPS363F | Distributed Systems and Parallel Computing | 5 | |
| COMPS380F | Web Applications: Design and Development | 5 | |
| COMPS381F | Server-side Technologies and Cloud Computing | 5 | |
| COMPS382F | Data Mining and Analytics | 5 | |
| COMPS390F | Creative Programming for Games | 5 | |
| COMPS413F | Application Design and Development for Mobile Devices | 5 | |
| COMPS492F | Machine Learning | 5 | |
| ELECS305F | Computer Networking | 5 | |
| ELECS363F | Advanced Computer Design | 5 | |
| ELECS425F | Computer and Network Security | 5 |
