Motion Tracking Glove for Human-Machine Interaction: Grasp & Release
Bibliographic record
Abstract
<p>A common problem seen among the lower extremity paralyzed individuals and the elderly with subsequently reduced movement abilities is that they must rely on others to do a simple task such as getting a glass of water from across the room. As a result of their physical limitations, they lose their independency and more so become burden for the others. Data Glove Controlled Dynamic Robotic Arm can virtually restore their movement abilities without them having to move from their place at all. Data Glove Controlled Dynamic Robotic Arm is composed of two major sophisticated systems that include the wearable Data Glove Dynamic Controller and the Wirelessly Moveable Robot Arm with built-in visual feedback system. In this project, a Data Glove has been designed with two flexor sensors, signal conditioning circuit, 3-axis accelerometer and 1-axis gyroscope IMU and Arduino Duemillanove ATmega328 microcontroller. In addition, a simulated stick figure virtual model of the Robot arm unit has been developed in Arduino-Processing IDE interface. For the implementation of the Data Glove controller, the flex sensors has been mounted on the index finger and thumb of the data glove. These sensors are variable resistors that outputs decreased resistance value when bent. Connecting these sensors through a signal conditioning circuit, two analog voltage signals are extracted that range between 0V-5V. Thus, when the user bends a finger, a corresponding analog voltage is generated. Feeding these analog inputs to the microcontroller ADC subunit, digital representation of the signals can be obtained. Based on the digital value corresponding to specific analog voltage outputs from the finger sensors, the microcontroller can be programmed to control the speed and planar rotational position of the servo motor linking the gripping fingers of the robot.</p>
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.372 | 0.007 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; both teacher heads agree on what is shown here.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".