Project #1: A Prototype to Detect Luggage Electronically Based on US Patent 7 535 358 B2

Introduction

Luggage security for airline passengers remains a major concern. According to the Wall Street Journal, 11,700 baggage theft and damages were reported to the Transportation Security Administration (TSA) in 2009.  The 2010 report from the International Air Transport Association (IATA) states that lost baggage costs the air industry more than US $2.5 billion every year, which equates to more than 25 million mishandled bags globally. Having recognized this problem, the goal of this project is to build a prototype of US patent 7 535 358 B2, method and apparatus for electronically tracking luggage. The patent states that the device would utilize the Global Positioning System (GPS) to both track and record the specific times and places that a piece of luggage is opened. Furthermore, the device needed to relay the location data along with a notification to the owner of the baggage in real-time. This alert would ensure that the owner would know that the bag had been compromised prior to its arrival at the destination. The unreliability of communications in transit requires that the device also have an onboard logging mechanism that would ensure that the conditions undergone by the bag during its transit timeline could be analyzed. In addition to the design details specified in the patent, data analysis tools were also need to be developed in order to digest the recorded information. These tools include web-based and smart-phone applications. 

Design

Several aspects of the design were considered: tracking, logging, transmission of data, receipt of data, and the analysis of data. Having researched various hardware options to closely resemble the desired functions, two options were proposed as solutions to the design problem. Option 1 shows a possible design without the use of GPS and SMS capabilities whereas Option 2 has SMS and GPS capabilities. Option 1 ensured that the bag open/close conditions would be logged on-board when external communications, or notification capabilities are unavailable. Option 2 was a full system with logging and notification capabilities, and is the design that most closely resembles the patent. In this project, Option 2 where wireless communication is assumed feasible has been implemented. The recent relaxations in the laws pertaining to the use of wireless communication devices onboard aircrafts, would enable the deployment of luggage detection devices with wireless capabilities.

Data Analysis Tools

The data analysis tools had three parts: web-based tools to view the logged data, a Twitter feed to log data sent in real-time, and an Android app to view the Twitter data. All these tools ensured that the user had ample resources to view the status of the bag in transit, and to view the SD card data once it had reached its destination. The figure below illustrates the analysis tools. The figure is broken into two parts: real-time and post flight. The real-time data that is relayed to the Twitter feed is scrubbed by the Android app and thus would give the user the capability to view the last known location of the bag. The app was developed to provide an easy to use interface for the user that can be summoned at any time to view the bag status. The web-based, post-flight tool allowed the visualization of the logged SD card data in graphical form along with important data pertinent to the transit timeline of the bag.

Data Analysis Tools

Implementation

Hardware Implementation

In the implementation stage of the design, several key factors had to be considered in order to ensure the device performed to specification. The primary concern was that of power. The device had to function continuously for a period of at least an hour for demo purposes while continually logging the sensor and GPS data along with the occasional SMS transmission. Another consideration was that of form factor. The device had to be compact and discrete in the bag. The components had to also be housed in a durable enclosure to ensure that rough handling of the baggage would not destroy the device. The following section details the various components utilized in the prototyping of the device.

Parts List

Part	Function
Arduino Uno	Microprocessor
Sparkfun GSM cellular module with patch antenna	Wireless communications
Adafruit SD Logger Shield	Logging of data on SD card
EM-406A GPS Module	GPS latitude and longitude data
Photocell	Light sensor
Reed Switch	Bag flap contact sensor
6 NiMH AA Rechargeable batteries	Power supply
Otterbox	Enclosure

Overall Device Topology

Software Implementation

Flow Chart

Web SD Data Analysis

A custom website was created to allow for the analysis of the data logged on the SD card.  After arriving at destination, the traveler would insert the SD card into their computer and summon the webpage. The website has an up-loader tool which allows the user to browse to the location of the SD card, and upload the most recent logged file. Once uploaded, the webpage would automatically parse the data and present the data in graphical form. Two graphs would be presented: light sensor, contact sensor. The presented data would indicate if the bag had been compromised in transit. Beneath the two graphs, a table will appear consisting of only the data that indicates the luggage might have been opened. We wanted to make our website very user-friendly so this way the user can look at the graphs to check if the bag has been opened and look at the table for all the specifics.

Web Analysis Tool

Testing And Results

Once the hardware assembly was completed, various functional tests were carried out to ensure the proper operation of the bag security device. The sensors were tested to ensure that the light and contact sensor readings were being recorded accurately on the SD card. The device was successfully initiating a SMS on a bag open event. The data logged on the SD card was corroborated with the twitter feed to ensure accuracy. The picture below displays a screenshot of the Twitter feed with updates on when the bag was opened.

It must also be noted that there is a delay of about 2 minutes between the bag open event and the notification being posted on twitter. This can be attributed to propagation delays in the twitter system and various other transmission delays beyond control. The bag was also driven in a car while creating bag open events to test the accuracy of the latitude and longitude data that was acquired. The error in the GPS was about 15 feet which was the typical value for consumer GPS devices.

Conclusion

The accomplishments of the project surpassed expectations and the prototype was successfully developed and tested according to the functions stipulated in the patent. Having developed a working prototype, the device could be marketed to luggage manufacturers as an enhancement to their products to provide a layer of security for their luggage. As per the current design solution, there are no sleep mechanisms or ways to remotely cycle the power states. In any event, the project demonstrated the feasibility of such a device and the overall cost effectiveness of prototyping such a device.

Project # 2: Implementation of Wireless Body Area Network for Healthcare Monitoring

Abstract

The rapid growth of wireless technologies and personal area networks has enabled the continuous healthcare monitoring of mobile patients using compact sensors that collect and evaluate body parameters and movements. These sensors constitute a body area network (BAN) where patients’ vital signs are collected and reported wirelessly to a base station. Once the data is received, it is displayed or stored in a database for future use. The use of BANs is to provide the users with logging of patients’ critical vital signs, and also to provide primary healthcare providers a snapshot of the wearer’s health.

 The goal of this project was to investigate the feasibility of the inexpensive construction, and use of a BAN.  A BAN, consisting of two nodes and a base station was successfully built and tested using open source and inexpensive hardware to measure pulse rate body temperature and patient’s location. Each node consisted of a pulse sensor, a temperature sensor, a GPS module and a Zigbee wireless modem packaged together. The nodes were designed to incorporate other sensors, such as an accelerometer, in the future.  The base station consisted of a receiving Zigbee modem and a Wi-Fi module.  The captured data was inserted into a MySQL database where a webpage with a graphing application programming interface (API) was used to display the data. The system has been successfully tested in real time where data was successfully obtained and displayed. Future enhancements to safeguard the data, including the encryption of the patient data is under investigation.

Network Architecture

The Wireless Body Area Network (WBAN) was implemented using a single hop star topology in beacon mode (data being sent continuously without interruption) where sensors collect data and send it to the base station which is the task manager of the network. The proposed WBAN architecture is shown in the figure below. Two individual body sensor nodes serving as transmitters have been designed to collect, process, and transmit the pulse rate, body temperature, and the patient’s location signals in real time. The system operates within a range of 30m from the base station.

The base station which is the network coordinator manages the activities of individual nodes by periodically requesting data. In addition to data integration and analysis, the base station also relays processed data to display devices and PDAs. The base station is equipped with an Arduino Uno Microcontroller for system coordination, a receiving ZigBee module and a Wi-fi module for wireless communication and data transmission over the 802.11b/g wireless networks which make it possible to access the collected data via the internet.

Transmitter

To achieve a power efficient network, open source and low power consumption hardware were used to implement the transmitter. Each transmitting node comprises one off-the shelf XBee wireless module, one pulse sensor, one temperature sensor, and one GPS module. The XBee wireless module operates on the 802.15.4 protocol at a frequency of 2.4GHz with a power output of 1mW and a data transmission rate of 250kbps which ensures the wireless nature of the network. One pulse sensor wearable on the ear or on a finger with a current consumption of 4mA at 5V , 16mm of diameter, 3mm of thickness, and a cable length of 609mm. The pulse sensor collects data from the pulse rate. One TMP36 analog temperature sensor with a voltage output of 1.75V at 125^°C to measure the body temperature. One GTPA013 ultimate GPS module with a -165dBm sensitivity and only 20mA current draw; this determines the location of the subject at all time. Each transmitting node is powered up using a 9V battery and the data collected is wirelessly sent to the base station for processing, display, and storage.

Transmitting Node

User's Interface

The User interface which is a display website was designed using the php and html code. The software intended to be easy for medical personnel to use and provides enough details on patient Pulse rate, blood pressure and location. The sensor device on the patient transmits raw data to the receiver which in turn sends the data wirelessly to the MySQL database using a wifi shield. Whenever, the   database gets new data from the device, it refreshes the page and displays the new data in the format that the user can understand. And also, include is a feature to store all the Data in server so it can be used future reference.

Experimental Results

To check the functionality of the devices, the following experiments were made. As shown on the figure below, the comparison between the industrial and the experimental pulse sensors was performed. The results obtained from both sensors are almost similar, that is a good indication for the functionality of the project. The graph also shows the result received from the temperature sensor. 

Body Temperature Data

Pulse Variation with Different Activities

Conclusion & Future Work

The system was built and successfully tested in real time where data was successfully captured and displayed. At this stage the project mainly focused on the collection of the data for pulse rate, temperature, and location from patient. The captured data is made available to the user through a graphing application programming interface (API). Future enhancements to safeguard the data, including the encryption of the patient data is under investigation. The communication between the transmitter and the base station is crucial to collect the data without any interruption. The network works within the range of 30 meters to have the best result. Power consumption of the devices is one of the most important phases of the project. Production of power using body temperature or from physical movements of the patient is the second phase of the project. For the now, the base station is powered up using a USB cable that connects with computer, but the transmitter uses a 9V battery to operate.  Finally, the design was completed using the low cost or off-the-shelf hardware. 

Project #3: Detecting Falls Among Elderly Patients in Nursing Homes Using Wireless Sensors Networks

Abstract:

Accidental falls among the senior population are the leading cause for seniors’ admission to hospitals.  Wireless Sensor Networks (WSNs) can be used to efficiently detect falls of senior patients in nursing homes. While some fall detection methods use only on the acceleration of the patient, others register acceleration and body position to detect falls.  In this paper, we describe a novel alert system created using WSNs capable of detecting falls based on the body position and bed occupancy. The system was created according to data collected in MoteView from seven test subjects.  Accordingly, it was designed to sense three possible conditions:  (1) patient being active; (2) patient lying in bed; or (3) if the patient has fallen down.  The experimental portion of this research was performed at a nursing facility to further validate measurements previously collected in the laboratory.  Furthermore, the system has been tested on three subjects for different types of falls and was found to detect all types of falls with high accuracy. In order to provide caregivers with constant alerts regarding patients’ conditions a graphic user interface was created in LabView. Design of this system maximizes the capabilities of Memsic’s Wireless Sensor Network Developmental Kit consisting of MICAz and MIB520 base station.  Overall, the system provides a very simple and effective solution that yields high accuracy for detecting falls.  

Approach:

• Developed an algorithm that detects falls based on the body position of the patient and bed occupancy 
• Tailored the design of the system to the technical capabilities that were available from Crossbow Wireless Sensor Network Development Kit and LabView 

Algorithm For Detecting Falls:

Two separate devices were designed and built. One to detect the patient's body position-if the patient's torso is upright or in a horizontal position and the other to determine whether the bed is occupied or not. These situations resulted in three possible outcomes

1. Patient is active

2. Patient is lying in bed

3. Patient has fallen down

Table 1: Fall Detection Depending on 4 events 

IF the position of patient torso IS	AND IF the patients bed IS	Then the situation IS
Upright	Occupied	PATIENT ACTIVE
Uptight	Unoccupied	PATIENT ACTIVE
Horizontal	Occupied	PATIENT IN BED
Horizontal	Unoccupied	PATIENT HAS FALLEN DOWN

Axis orientation of the bi-axial accelerometer which is placed on the patient

Note: The sensor records the acceleration due to gravity

Hardware Implementation:

•    MIB520 Base Station

• Set of MICAz Motes equipped with MTS400CC sensor board

• MoteView Software

Data Collection:

The data was collected from seven male subjects performing various activities. The activities in question were walking, walking with a cane, jogging, tying shoes, standing straight, lying on the back, lying on the left side, lying on the right side, sitting, eating on a table.

The analysis of the data led to the establishment of the threshold acceleration (Accy):

• If Accy > 7m/S² then the patient is in upright position (patient active)
• If Accy < 4m/s² then the patient is in a horizontal position (patient lying)

Experimental Results:

Data collected from one test subject was graphed for various activities. The resulting graph was in agreement with the established threshold for the acceleration.

The established threshold was further tested for three additional subjects performing different activities and the results were satisfying.

Bed Mechanism

A black box was built for the bed mechanism. Essentially, a switch placed on the patient’s bed is used to detect the occupancy of the bed. Once the patient lies on the bed, the switch will be depressed; the switch is connected in a DC circuit with 4.5V battery and 8 LED lights. Led lights are placed in a box that is constructed from opaque material to prevent light coming from outside. The only light allow inside the box is the light from the LEDs. MICAz node is placed in the box and it is sending light intensity data to MIB520 base station. If the light intensity is under 1Lux, that is a signal LED’s are not emitting light and that bed is not occupied. If light intensity is above 10Lux that is a signal that LED’s are emitting light and that bed is occupied.

  

User's Interface-LabView Graphical

There are currently two graphical user interfaces created. In the first interface LABVIEW receives data from Memsic WSN and MICAz node that is placed on patient chest, and then it compares values from acceleration in y-axis with our set thresholds. Depending on those values, two alerts are possible: patient is lying down (y_{acc <}  4m/s² ) or patient is active (y_acc>  7m/s² ). Furthermore these values have been coupled in two consecutive time intervals ( y_acc (T), y_acc (T+1)) in order to get more accurate information. Second graphical user interface has been created for data received from node that detects bed occupancy. If flux is under 1lux that there will be an alert that the bed is unoccupied. If Light flux is above 10 Lux second alert will prompt that bed is occupied. 

Flow Chart

Conclusion

Accidental falls represent serious issue among the senior population. Timely detection of these events in nursing homes can significantly improve treatment of injuries and reduces costs to healthcare system. The aforementioned paper is a clear demonstration of a simple and effective Wireless Sensor Network (WSN) system which detects senior patients falling in nursing homes. Behavior of senior patients was observed in nursing home and used to create list of possible activities that senior patients engage in daily activities.  Then the system was created based on data from seven test subjects, and then system capabilities were verified on three test subjects. System has detected every fall, and didn't have false alarms.