41,337 research outputs found
A Low-Cost Tele-Presence Wheelchair System
This paper presents the architecture and implementation of a tele-presence
wheelchair system based on tele-presence robot, intelligent wheelchair, and
touch screen technologies. The tele-presence wheelchair system consists of a
commercial electric wheelchair, an add-on tele-presence interaction module, and
a touchable live video image based user interface (called TIUI). The
tele-presence interaction module is used to provide video-chatting for an
elderly or disabled person with the family members or caregivers, and also
captures the live video of an environment for tele-operation and
semi-autonomous navigation. The user interface developed in our lab allows an
operator to access the system anywhere and directly touch the live video image
of the wheelchair to push it as if he/she did it in the presence. This paper
also discusses the evaluation of the user experience
Transportation mode recognition fusing wearable motion, sound and vision sensors
We present the first work that investigates the potential of improving the performance of transportation mode recognition through fusing multimodal data from wearable sensors: motion, sound and vision. We first train three independent deep neural network (DNN) classifiers, which work with the three types of sensors, respectively. We then propose two schemes that fuse the classification results from the three mono-modal classifiers. The first scheme makes an ensemble decision with fixed rules including Sum, Product, Majority Voting, and Borda Count. The second scheme is an adaptive fuser built as another classifier (including Naive Bayes, Decision Tree, Random Forest and Neural Network) that learns enhanced predictions by combining the outputs from the three mono-modal classifiers. We verify the advantage of the proposed method with the state-of-the-art Sussex-Huawei Locomotion and Transportation (SHL) dataset recognizing the eight transportation activities: Still, Walk, Run, Bike, Bus, Car, Train and Subway. We achieve F1 scores of 79.4%, 82.1% and 72.8% with the mono-modal motion, sound and vision classifiers, respectively. The F1 score is remarkably improved to 94.5% and 95.5% by the two data fusion schemes, respectively. The recognition performance can be further improved with a post-processing scheme that exploits the temporal continuity of transportation. When assessing generalization of the model to unseen data, we show that while performance is reduced - as expected - for each individual classifier, the benefits of fusion are retained with performance improved by 15 percentage points. Besides the actual performance increase, this work, most importantly, opens up the possibility for dynamically fusing modalities to achieve distinct power-performance trade-off at run time
A sub-mW IoT-endnode for always-on visual monitoring and smart triggering
This work presents a fully-programmable Internet of Things (IoT) visual
sensing node that targets sub-mW power consumption in always-on monitoring
scenarios. The system features a spatial-contrast binary
pixel imager with focal-plane processing. The sensor, when working at its
lowest power mode ( at 10 fps), provides as output the number of
changed pixels. Based on this information, a dedicated camera interface,
implemented on a low-power FPGA, wakes up an ultra-low-power parallel
processing unit to extract context-aware visual information. We evaluate the
smart sensor on three always-on visual triggering application scenarios.
Triggering accuracy comparable to RGB image sensors is achieved at nominal
lighting conditions, while consuming an average power between and
, depending on context activity. The digital sub-system is extremely
flexible, thanks to a fully-programmable digital signal processing engine, but
still achieves 19x lower power consumption compared to MCU-based cameras with
significantly lower on-board computing capabilities.Comment: 11 pages, 9 figures, submitteted to IEEE IoT Journa
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