ABSTRACT-New smartphone technologies are emerging which combine head-mounted displays (HMD) with standard functions such as receiving phone calls, emails, and helping with navigation. This opens new opportunities to explore cyber robotics algorithms (robotics sensors and human motor plant). To make these devices more adaptive to the environmental conditions, user behavior, and user preferences, it is important to allow the sensor-equipped devices to efficiently adapt and respond to user activities (e.g., disable incoming phone calls in an elevator, activate video recording while car driving). This paper hence presents a situation awareness system (SAS) for head-mounted smartphones. After collecting data from inertial sensors (accelerometers, gyroscopes), and video data (camera), SAS performs activity classification in three steps.Step 1 transforms inertial sensor data into a head orientationindependent and stable normalized coordinate system. Step 2 extracts critical features (statistical, physical, GIST).Step 3 classifies activities (Naive Bayes classifier), distinguishes between environments (Support Vector Machine), and finally combines both results (Hidden Markov Model) for further improvement. SAS has been implemented on a sensor-equipped eyeglasses prototype and achieved high accuracy (81.5%) when distinguishing between 20 real-world activities.
Emerging wearable technologies offer new sensor placement options on the human body. Particularly, head-mounted glasswear opens up new data capturing possibilities directly from the human head. This allows exploring new cyber-robotics algorithms (robotics sensors and human motor plant). Glass-wear systems, however, require additional compensation for head motions that will affect the captured sensor data. Particularly, pedestrian dead-reckoning (PDR), activity recognition, and other applications are limited or restricted when head-mounted sensors are used, because of possible confusion between head and body movements. Thus, previous PDR approaches typically required to keep the head pointing direction aligned with the walking direction to avoid positional errors. This paper presents a head-mounted orientation system (HOS) that identifies and filters out interfering head motions in 3 steps.Step 1 transforms inertial sensor data into a stable normalized coordinate system (roll/pitch motion compensated).Step 2 compares walking patterns before and after a rotating motion.Step 3 eliminates interfering head motions from sensor data by dynamically adjusting the noise parameters of the extended Kalman filter. HOS has been implemented on a Google Glass platform and achieved high accuracy in tracking a person's path even in the presence of head movements (within 2.5% of traveled distance) when tested in multiple real-world scenarios. By eliminating head motions, HOS not only enables accurate PDR, but also facilitates the task for downstream activity recognition algorithms.
Abstract-In many robotics applications, knowing the material properties around a robot is often critical for the robot's successful performance. For example, in mobility, knowledge about the ground surface may determine the success of a robot's gait. In manipulation, the physical properties of an object may dictate the results of a grasping strategy. Thus, a reliable surface identification system would be invaluable for these applications. This paper presents an Inertia-Based Surface Identification System (ISIS) based on accelerometer sensor data. Using this system, a robot actively "knocks" on a surface with an accelerometer-equipped device (e.g., hand or leg), collects the accelerometer data in real-time, and then analyzes and extracts three critical physical properties, the hardness, the elasticity, and the stiffness, of the surface. A lookup table and k-nearest neighbors techniques are used to classify the surface material based on a database of previously known materials. This technique is low-cost and efficient in computation. It has been implemented on the modular and selfreconfigurable SuperBot and has achieved high accuracy (95% and 85%) in several identification experiments with real-world material.
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