Mentor: Zhuo Chen
Students: Misha Kutsovsky, Sakshi Goel (Poster, Video)
This project aims at using environmental cameras to detect a user's activities inside a gym, e.g. push-ups, squats, etc. It could also count and record different activities a user has performed, The goal is to analyze a user's action and give user real-time feedback.
Similar to the "Camera-based recognition" project below, this project could use existing implementation on volumetric template matching. The students may also develop new algorithms (e.g. DNN). The real-time system can be built on top of Gabriel, but the client part has to be rewritten if we use non-Android based cameras.
Mentor: Brandon Amos
Students: Shruti Dhoot, Vrushali Bhutada (Poster, Video)
This project provides naive assistance to visually impaired Google Glass users by providing spoken cues of objects they are looking at. The Google Glass device will stream video to a cloudlet, which will detect objects in the video frames and send object names back to the device. This project will use existing open source implementations of state-of-the-art computer vision models using deep neural networks such as Teradeep: https://github.com/teradeep/demo-apps and https://www.youtube.com/watch?v=_wXHR-lad-Q.
The streaming can optionally be done with the Gabriel framework: https://github.com/cmusatyalab/gabriel
Environments: Android and Linux
Skills Learned: Python, Caffe (C++/Python) or Torch (Lua), OpenCV
Mentor: Kiryong Ha
Students: (a) Junjue Wang, Xinkai Wang (Poster, Video)
Students: (b) Varun Saravagi, Harsha Rastogi (Poster, Video)
In addition to the latency benefit, a cloudlet is a perfect place for caching large amount of data with locality. In this work, the students will implement interactive hyper-lapse simulation using Google Street View. A reference prototype is implemented and open sourced at https://github.com/TeehanLax/Hyperlapse.js. The students will extend this work to enable an interactive hyper-lapse system. A user will choose the route interactively as the hyper-lapse video proceeds and the cloudlet will provide a cache for the Google Street View to speed up downloading of the image data (reference demo video: https://vimeo.com/63653873).
Mentor: Babu Pillai
Students: Subramanian Natarajan, Priyanka Kulkarni (Poster, Video)
This project seeks to use camera based sensing of gestures in an interactive game setting. The idea is to allow the user to control the game using large hand and body motions, observed by a fixed camera (e.g. on a laptop). Example: Gestris (gesture controlled tetris) from a few years ago. This system used an unmodified tetris-style game, and a pair of cameras to allow two players at a time. The computer vision code used volumetric template matching -- a fairly simple, but computationally expensive algorithm that is fairly easy to train. This will not run in interactive time on a laptop, and will need cloudlets. The goal of this project is to demonstrate some sort of gesture-controlled interactive game. Alternatively, the system can be designed to respond to other camera-sensed inputs, such as a laser pointer on real objects or projected screen.
Mentor: Babu Pillai
Students: Joel Cao, Caglayan Gemici (Poster, Video)
This project will attempt to apply superresolution techniques to produce a "live" view that is higher resolution than the native camera on a mobile phone or Google glass device. The idea behind superresolution is that multiple images that are slightly offset can provide clues to the ground truth at a sub-pixel level. There are many algorithms to do this, including some that are supported in OpenCV. The goal of the project is to demonstrate some sort of application showing an enhanced live view of what the camera is capturing (e.g., a magnifier app).
Mentor: Khalid Elgazzar9. Activity Recognition with Mobile and Wearable Devices
Students: Sahil Shah, Aditya Gabbita (Poster, Video)
Finding a missing child in the crowd is a crucial and non trivial task. This project aims to reduce the burden on desparate parents and alleviate their unpleasant experience. Parents will have to share a few recent photos of the missing child with a cloudlet server and ask for help from the crowd around the area where the child is believed to be missing in. The cloudlet will build a classifier from the child's images and use it to match faces on videos and images coming from the crowd's input. Once a match is detected the parents will be notified of where the child is last seen. The processing must be super quick as the child might move far from the current location until detection is confirmed. Alternative (extention): upon confirmed detection, the cloudlets might also provide a tracking functionality of the child.
Hint: Accuracy is important but not a major deal in the project -- the focus is on how fast the process would be (with reasonable accuracy).
- Build a face recognition model (from the child's images)
- Stream video from the Google glass to the cloudlet server
- Nexus 6 sends scene images each 10 secs to the cloudlet server
- Find the child in the video frames or images coming from the Glass or the phone
- Send a notification (with the location) to the parent's smartphone (another Nexus 6)
Mentor: Jim Osborne
Students: Sarthak Dubey, Pallavi Santhosh Kumar (Poster, Video)
Used together, mobile/wearable sensor readings and task/time/motion scripts could become a powerful yet straightforward way to detect and log a person’s activities. As an early step, we want to develop an interactive system for associating those two types of data.
This project has four main pieces:
1. Design and implement a scheme for a user to specify his/her activities in terms of a sequence of timed actions. It should allow for recursive decomposition and for re-use of “atomic” actions (e.g., sitting down, lifting an object, opening a door, walking between refrigerator and stove, etc.). This might be captured in or transferred to a calendar program for convenience.
2. Design and implement a scheme for interrogating the user* via his/her smartwatch about the task that is about to start, currently being executed, or just completed. It could be an inquiry with a limited number of responses or a simple yes/no confirmation.
3. Design and implement a scheme to associate smartwatch sensor data with the tasks as logged via EMA. Doing so establishes a labeled data set for a supervised machine learning algorithm (see below).
4. Design and implement a machine learning approach to automatically identify some actions or whole tasks based on the smartwatch sensor data. As a bonus, upgrade the user interrogation scheme to do EMAs for confirming/refuting the identified actions/tasks.)
* (Note: the general term for this form of user interaction is “ecological momentary assessment” or EMA. There is a substantial literature on the technique to which the course instructors have contributed. A good introductory slide set and paper are available here: https://www.dropbox.com/sh/76x5uaekcd5xo38/AADg2KOeVTmxePK5bOzAk6Nsa?dl=0)
Mentor: Brandon Taylor
Students: Shekhar Sharma, Siddharth Shah, Aravind Selvan (Poster, Video)
Blind individuals often struggle to make geographic sense of unfamiliar locations. Tactile maps can provide information about an area, but are typically passive objects with no awareness of the user's current location. Smartphones with location-based navigation apps using text-to-speech functionality can direct individuals from point A to
point B, they provide little context or sense of place. This project will explore how tactile maps and location based apps can be combined to provide a better real-time navigation system.
Students will use existing tools (www.tactilemaps.net <http://www.tactilemaps.net>) to create tactile maps. They will explore
strategies for embedding touch responsive regions in the maps for use with a companion smartphone application. They will explore how to leverage the smartphone's location sensors, audio and haptic IO capabilities to provide relevant and useful feedback to the users.
Most of the work itself will be in designing an Android application, though some amount of 3D printing will be required.
Mentors: Asim Smailagic and Dan Siewiorek
Students: Siyan Zhao, Kedar Amladi, Vinay Shankar (Poster, Video)
Blood pressure (BP) is one of the key measurements used to monitor patient health. The operation of current blood pressure monitors are usually described in manual with small font, unclear images, and complex interactions. These manuals, often over 30 pages long for even basic BP monitors, also contain several pages of error codes in an attempt to inform the patient of operational mistakes.
Glassistant is a Google Glass based vision application to guide the use of a wrist worn BP monitor. The current prototype, based on cloudlet infrastructure, uses vision processing to determine if the BP monitor is correctly positioned (right or left wrist, bottom of wrist, buttons facing user, proper elevation with respect to the heart). Voice commands and visuals are used for each step in the process. Machine vision techniques have been used to understand room light level, background clutter, detecting device orientation, detecting the device screen, and using optical character recognition to extract the contents of the screen.
The project will focus on developing and implementing a protocol to instruct and track the use of the BP monitor, to recognize errors, and to guide corrective actions. Usability evaluation will consist of a pilot study.
Mentors: Asim Smailagic and Dan Siewiorek
Students: Nat Jeffries (Poster, Video)
Gait variability, defined as fluctuations in gait characteristics from one step to the next, has been estimated to affect 35-45% of community-dwelling ambulatory older adults. Greater gait variability is related to less confidence in walking and lower levels of daily physical activity, and it is an independent predictor of falls and future mobility disability.
While slow gait speed may be a global indicator of balance problems in walking, altered timing of steps (gait variability) and abnormal postures (biomechanical abnormalities) signal the onset of mobility disability. Sensors available for this project include a Kinect, a video camera, and an Inertial Measurement Unit (IMU – includes accelerometers and gyroscope).
Exercises are used to clinically assess gait. For example a typical exercise, the Sit to Stand, measures how long it takes a person to sit down then stand up a certain number of times. The Sit to Stand exercise can be measured with a Kinect while the Get Up and Go (a timed walk) exercise can be measured by the IMU. Using these typical gait exercises we will develop a gate analysis and abnormalities detection algorithm. The algorithm will be evaluated in a pilot study using mechanisms to vary gait such as adding a weight to one leg.