PRE2016 3 Groep10

Advanced Elderly Emergency System (A.E.E.S.)

This is the wiki page of group 10 of the USE course: Project Robots Everywhere at the Eindhoven University of Technology. Here we will thoroughly describe all aspects of our project, in which we design a wearable fall-detecting device for elderly people.

Design team

We have a diverse team of 6 people from different majors of the TU/e:

Name	Student ID	Department
Lennard Buijs	0959903	Mechanical Engineering
Bram Grooten	0885158	Applied Mathematics
Ken Hommen	0911594	Industrial Engineering
Pieter van Loon	0861532	Software Science
Steef Reijntjes	0944701	Electrical Engineering
Man-Hing Wong	0944285	Electrical Engineering

Problem statement

Problem

In the Netherlands we have more and more elderly people. For them it can be more difficult to balance themselves when standing or walking, so it regularly happens that they fall. When they do, they often have trouble standing up. Also, they have a higher chance of injuries, with hip fractures being the most common. The problem especially arises if these elderly are severely injured by a fall, and therefore can't get up to reach for help.

Solution

A device that should be worn by, mostly, elderly that detects when one has fallen. It can automatically send a warning to ‘ICE’-persons or even call 112. Automatically sending its location along with it. A microphone and camera can in this case be used to observe the situation even faster. By connecting the device to the internet, this all can be made possible even faster. Also the device can ‘ask’ questions to the owner in case of emergency, which can be answered by simply talking back.

Objectives

To effectively and successfully end this project, a list of objectives is created to ensure weekly improvements, which will help in obtaining a final product. List of objectives:

• Research state-of-the-art technology
• Establish list of requirements
• Design the system that follows every requirement, this would be our ideal deliverable
• Create a final presentation, explaining our design

USE Aspects

User

The user of the Advanced Elderly Emergency System are the elderly. Since the number of elderly is increasing, more elderly people will need a way of calling for help when an emergency occurs. Nowadays, an elder may have an arm wrist or necklace which has a button to call for help. However, in the future, there will be too many elderly for the number of nurses or doctors. By asking questions, the doctor's job will be easier, which means it will also help the enterprise.

Research

State of the art

Medical Alert Systems

Currently there a few different wearable emergency devices for elderly. All of them have slightly different functions. Firstly, there is the Medical Guardian. The Medical Guardian is considered the best medical alert system available. The Medical Guardian (Premium) is a medical alert system that can be worn as an arm wrist, belt or necklace and has the following features:

• Fall detection that will call for help when the patient has fallen
• A button that will call for help and contacts you with an employee of Medical Guardian
• GPS-tracking
• Heat-sensor in case of fire

Fall detection

The fall detection is most important to this research, since it is one of the main features of the Advanced Elderly Emergency System. Currently, most fall detection systems utilise accelerometers and gyroscopes. With these sensors the movement of the patient can be measured. A sudden change of direction can then be also be measured and calculated.

Most sensors have a certain waiting time before calling for help, because it checks for movement after a possible fall. If the patient moves, the system will not call for help. When an AI is added to the system, this waiting time can be reduced a lot, because the AI can ask the patient whether he or she has fallen.

In the A.E.E.S. the fall control system by F. Wu. et al. will be used. This system requires only a single triaxial accelerometer and therefore uses significantly less power than the systems with gyroscopes. This means the elderly people will have to charge their alert system less. More on this fall detection system and how it works is explained in chapter 6.4 "Implementation of fall detection sensor".

Speech recognition

Speech recognition is another important feature of the Advanced Elderly Emergency System. There are many applications for speech recognition. A very wellknown software is Siri. Siri is able to recognise one's voice and hear what the person is asking.

Approach

The A.E.E.S improves on the state of the art technology by implementing an AI into the system which helps the user in case of an emergency where the user is unable to manually activate the needed functions on the device. This AI determines whether or not certain actions need to be taken depending on the current situation/enivornment. These actions include, but are not limited to:

• Calling emergency services
• Contacting family members/friends
• Asking the user what his/her state is

Requirements

These are components that our design will most likely need:

• Wearable, for example one of the following:
  • Belt clip
  • Wrist band
  • Necklace
• Microphone, for speech recognition
• Speakers, so it can speak to the user
• GPS to track location
• Internet and phone connections
  • Call emergency services
  • Contact next of kin
  • Share location with next of kin
• Sensors to detect falling:
  • accelerometer
  • barometer
  • gyroscope
• Air quality measuring sensor
• Button to manually trigger emergency state

Design options

We plan to design a device to attach to one's belt which will detect the fall. Then this belt clip could:

• have it's own microphone and speaker to communicate with the user.
• or the belt clip could be connected to a smartwatch app. This smartwatch will then need to have a microphone and a speaker. Many smartwatches exist which don't have a speaker, thus we would need to look for a specific watch that does have one.
• or it could connect with a smartphone app. This is simpler in design for us, and most smartphones both have a microphone and speakers. However, many elderly won't have their smartphone with them at all times, if they even have a smartphone.
• or we could make our own wrist band with a built-in microphone and speaker to communicate with the user.

It seems like we should definitely make the belt clip, and see what we can add because of our limited time. But, the functionality of this belt device alone already exist, as we have seen in our research. To really add something to the state-of-the-art, we need to combine it with some form of AI. Thus another option is starting with the AI functionality.

AI functionality

The AI in the device is activated when the device registers a falling motion, or when the user presses the action button. This AI will then immediately contact emergency services to inform them that an elderly person fell. The emergency services then know the place/situation of the elderly person, and they will act accordingly by for example sending out an ambulance. After this, the AI will ask the person if they are okay, and eventually if the AI should contact family/friends. If the AI does not receive feedback from the user, the AI will send a message to family or friends that something is wrong with the user.

Implementation of fall detection sensor

Design by Falin Wu et al.

The fall detection sensor in the A.A.E.S. will be fully based on the fall detection sensor proposed in "Development of a Wearable-Sensor-Based Fall Detection System" by F. Wu et al. The advantages of using this sensor are:

• Can be worn on the waist, but does not have to be fixed in position
• Only uses a single triaxial accelerometer for fall detection
• Is power efficient, making the elderly able to travel without constantly having to recharge it.

This design is an acceleration threshold based design, which will be further explained in Chapter 6.4.2 "Fall Detection Algorithm". The reliability of this sensor is fairly high. In Table XXX the test results of the system test are shown. As you can see, the sensor detects a fall 220 out of 240 times, resulting in an average sensitivity of 91,7%. However, also notable is the number of false alarms when a person is jumping, walking or resting. Most elderly people that need a medical alert system will not regularly be jumping, but to limit the number of false alarms it is necessary to experiment with the number of a_threshold.

Table XXX: Test results of the design with different motion types

Fall Detection Algorithm

The fall detection algorithm consists of five states. The state diagram is the same as the original design by F. Wu et al., with the exception of state 5. The state diagram can be found in figure XXX.

State 1: Initial state

The acceleration a of the elder will be measured by the triaxial accelerometer. This acceleration will consist of a_x, a_y and a_z. The total acceleration can be calculated with the formula:

|a| = sqrt(a_x² + a_y² + a_z²)

g_before will be set to the current acceleration.

State 2: Acceleration Acquisition state

In this state the program will check if the current acceleration is higher than the set number a_threshold. It is necessary to do some experiments to find the right number for this a_threshold. When this acceleration is reached, the program will progress to state 3.

State 3: Impact state

When a person falls, it will reach a state of impact, after which he or she will be laying still. At the point where the person is laying still, the acceleration picked up by the accelerometer will equal g. Since it takes a while for a person to finish their falling, a t_threshold is necessary to minimize false alarms. This time will be set to at least two seconds. When the person has come to a stop and two seconds have passed, the program will progress to state 4.

State 4: Lying state

In the lying state, the system checks whether the person that has fallen has changed its angle relative to the ground. When the person starts by standing, he or she will have a rotation angle of 90 degrees after the fall. The rotation angle can be calculated by knowing the direction of the gravity g_before and the direction after g_after. g_after will be set to the acceleration after the fall, and the rotation angle can then be calculated using the formula:

θ = 2 arctan() (insert picture for formulas)

When the rotation angle is approximately 90 degrees, the program will progress to state 5.

State 5: AI state

In this state, the AI with speech recognition takes over. When the AI has done its job, the program will go back to state 1.

Figure XXX: State diagram of fall detection algorithm

Hardware Analysis

Measurement hardware used in this system include a motion sensor and a GPS and GSM service unit. Together with a microprocessor for processing data and a speaker/microphone set for human interaction, these units comprise the entire system which will be powered by a low-voltage battery (1200 mA with 3.7 V is enough to last a couple of days).

• An ADXL345 can be used as motion sensor and will typically cost €3.50.
• A SIM808 GPS GSM GPRS which is used for GPS and GSM service which costs around €40.00.
• A TI MCU MSP430F1611 microprocessor will control the whole system and imply the detection algorithm. This costs approximately €17.00.

Final design

Design description

Our final design is a wearable system, which can mostly be worn by elderly. This system consists of two parts. Those parts are each individually responsibly for different functionalities of the whole system. The lower part, attached on the belt, is responsible for fall detection, while the upper part, attached as arm wrist, should be held responsible for the speech recognition.

For determining the spot on the human body to put the lower part on, we had to look for a location what is most balanced on each time sequence. It had to be a place, what did not change much from its location when performing human actions. This part will be the main part, which is relatively bigger in size than the upper part on the arm wrist due the fact that it contains the microprocessor. The upper part is not suited for this functionality, since that would be less comfortable.

The upper part is an arm wrist fulfulling the speech recognition task. Artificial intelligence should be held into consideration when designing this part. The arm wrist should ask questions to the relevant person that just fell. Artificial intelligence can be used to have a conversation with the person based on the situation, and further on determine the state of the current situation. Based on the state of this situation, it can perform actions fully autonomous by notifying health instances in cases when needed. This can speed up the process to help the wounded elderly, or even save their lives.

Possible Expansions

Detecting tone of voice

By analysing the tone of the users voice it should be possible to make some assumptions about the severity of the situation

Learning users patterns

The system could learn from when an user often triggers the system without there being an emergency. It can for instance update it's thresholds or also take time of day and or the users patterns into consideration.

Literature

Other pages with important information on this subject:

URLs

Medical guardian emergency system

https://www.medicalguardian.com/product/premium-guardian

The Best Medical Alert Systems of 2017

http://www.toptenreviews.com/health/senior-care/best-medical-alert-systems/

Speech recognition library

http://arjo129.github.io/uSpeech/

Appendices

This section gives an overview of the progression and planning of our design project. This mainly concerns organizational as well as technical tasks, decisions and ideas that have been performed within our project environment to maintain a successfull and appropriate end result. Explanations on how certain decisions has been made by our design team can be found in the following, relevant sections:

• Appendix A - Project progress (log)
• Appendix B - Planning