Motion Tracking for Smart Home Care

Computational Infrastructure and Informatics Poster SessionHuman body motion capture has a wide range of applications and is being extensively investigated. Areas of application include virtual and augmented reality, biomechanics, sign language translation, gait analysis and graphics in movies and video games. The goal of this work is to develop an electronic device to translate arms motion and hand gestures into computer commands for smart home applications. This device is expected to improve the communication and lifestyle of elderly and disable people. We envision a device that is wearable, seamless and easy to use. Current state-of-the-art body motion capture employs high-speed and high-resolution cameras. Although this method is accurate and useful in laboratory settings, it requires the user to be inside the field of view of the cameras, a condition that is not always feasible during everyday activities. Instead, our approach relies on small sensors nodes that are worn on the wrists and around the waist. Inertial sensors such as accelerometers and gyroscopes have been employed before to develop wearable motion-tracking sensors due to their small size. However, they suffer from drift which causes the position estimations to have large errors. Ultrasonic sensors have also been employed to track motion. Although more accurate, ultrasonic sensors are affected by intermittent signal blockage produced by the body. Our approach is to combine these two sensing modalities in a way that the position estimation error is reduced. To that end, the outputs of the inertial and ultrasonic sensors are fused using a Kalman filter. The sensor nodes implement a multilateration algorithm that calculates the position of body-mounted sensors by measuring the time of travel of ultrasound bursts traveling between the sensor nodes. An electronic board for the sensor nodes have been designed, fabricated and programmed. The board measures 3.2 cm x 4.8 cm and includes a low-power microcontroller, a radio unit, a three-axis accelerometer, a two-axis gyroscope, an ultrasonic transmitter and an ultrasonic receiver. Our ongoing activities include the development of a 3D virtual simulation of a smart home. In the virtual smart home, various electronic devices such as computers, cell phones and household appliances like microwaves and televisions are networked for ubiquitous services. The wearable sensors capture the limb movements and relay this data to a central controller where it is interpreted to adjust the home environment. The sensors can also be used for emergency care by detecting any abnormal movements. Our approach will significantly improve current motion capture systems that are too cumbersome to wear or require the subject to be confined to a controlled environment or within the view range of the camera. Besides their use in smart home scenarios, the proposed wearable motion-tracking sensors can be used in biomechanic studies, virtual reality and interactive games

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Mobile forensic triage for damaged phones using M_Triage

Mobile forensics triage is a useful technique in a digital forensics investigation for recovering lost or purposely deleted and hidden files from digital storage. It is particularly useful, especially when solving a very sensitive crime, for example, kidnapping, in a timely manner. However, the existing mobile forensics triage tools do not consider performing a triage examination on damaged mobile phones. This research addressed the issues of performing triage examination on damaged Android mobile phones and reduction of false positive result generated by the current mobile forensics triage tools. Furthermore, the research addressed the issues of ignoring possible evidence residing in a bad block memory location. In this research a new forensics triage tool called M_Triage was introduced by extending Decode’s framework to handle data retrieval challenges on damaged Android mobile phones. The tool was designed to obtain evidence quickly and accurately (i.e. valid address book, call logs, SMS, images, and, videos, etc.) on Android damaged mobile phones. The tool was developed using C#, while back end engines was done using C programming and tested using five data sets. Based on the computational time processing comparison with Dec0de, Lifter, XRY and Xaver, the result showed that there was 75% improvement over Dec0de, 36% over Lifter, 28% over XRY and finally 71% over Xaver. Again, based on the experiment done on five data sets, M_Triage was capable of carving valid address book, call logs, SMS, images and videos as compared to Dec0de, Lifter, XRY and Xaver. With the average improvement of 90% over DEC0DE, 30% over Lifter, 40% over XRY and lastly 61% over Xaver. This shows that M_Triage is a better tool to be used because it saves time, carve more relevant files and less false positive result are achieved with the tool

Mobile forensic triage for damaged phones using M_Triage

Mobile forensics triage is a useful technique in a digital forensics investigation for recovering lost or purposely deleted and hidden files from digital storage. It is particularly useful, especially when solving a very sensitive crime, for example, kidnapping, in a timely manner. However, the existing mobile forensics triage tools do not consider performing a triage examination on damaged mobile phones. This research addressed the issues of performing triage examination on damaged Android mobile phones and reduction of false positive result generated by the current mobile forensics triage tools. Furthermore, the research addressed the issues of ignoring possible evidence residing in a bad block memory location. In this research a new forensics triage tool called M_Triage was introduced by extending Decode’s framework to handle data retrieval challenges on damaged Android mobile phones. The tool was designed to obtain evidence quickly and accurately (i.e. valid address book, call logs, SMS, images, and, videos, etc.) on Android damaged mobile phones. The tool was developed using C#, while back end engines was done using C programming and tested using five data sets. Based on the computational time processing comparison with Dec0de, Lifter, XRY and Xaver, the result showed that there was 75% improvement over Dec0de, 36% over Lifter, 28% over XRY and finally 71% over Xaver. Again, based on the experiment done on five data sets, M_Triage was capable of carving valid address book, call logs, SMS, images and videos as compared to Dec0de, Lifter, XRY and Xaver. With the average improvement of 90% over DEC0DE, 30% over Lifter, 40% over XRY and lastly 61% over Xaver. This shows that M_Triage is a better tool to be used because it saves time, carve more relevant files and less false positive result are achieved with the tool