Learn to See: A Microwave-based Object Recognition System Using Learning Techniques

Erdélyi, Viktor; Rizk, Hamada; Yamaguchi, Hirozumi; Higashino, Teruo

doi:10.1145/3427477.3429459

Cited by 11 publications

(2 citation statements)

References 13 publications

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“…There are many technologies using different sensors for sensing, such as cameras [19,35], capacitive sensors [48], RF sensors [20,64], light sensors [27,49,65], EMG sensors [21]. Ohnishi et al [47] captured images using a wrist-worn camera to identify handheld objects.…”

Section: Other Object Recognition Methodsmentioning

confidence: 99%

ViObject

Chen,

Lin,

Peng

et al. 2024

Proc. ACM Interact. Mob. Wearable Ubiquitous Technol.

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Knowing the object grabbed by a hand can offer essential contextual information for interaction between the human and the physical world. This paper presents a novel system, ViObject, for passive object recognition that uses accelerometer and gyroscope sensor data from commodity smartwatches to identify untagged everyday objects. The system relies on the vibrations caused by grabbing objects and does not require additional hardware or human effort. ViObject's ability to recognize objects passively can have important implications for a wide range of applications, from smart home automation to healthcare and assistive technologies. In this paper, we present the design and implementation of ViObject, to address challenges such as motion interference, different object-touching positions, different grasp speeds/pressure, and model customization to new users and new objects. We evaluate the system's performance using a dataset of 20 objects from 20 participants and show that ViObject achieves an average accuracy of 86.4%. We also customize models for new users and new objects, achieving an average accuracy of 90.1%. Overall, ViObject demonstrates a novel technology concept of passive object recognition using commodity smartwatches and opens up new avenues for research and innovation in this area.

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Section: Other Object Recognition Methodsmentioning

confidence: 99%

ViObject

Chen,

Lin,

Peng

et al. 2024

Proc. ACM Interact. Mob. Wearable Ubiquitous Technol.

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“…Based on the Universal Approximation Theorem [ 45 ], Neural Networks could be considered as being capable of approximating any arbitrary function, provided they were suitably complex. Due to performance benefits obtainable from its use, deep networks have been shown to define the state-of-the-art performance in many problem domains (e.g., [ 46 , 47 , 48 , 49 , 50 , 51 , 52 , 53 , 54 , 55 , 56 ]), in some cases even outperforming humans on the same task.…”

Section: Drone-based Flood Damage Assessmentmentioning

confidence: 99%

Drone-Based Water Level Detection in Flood Disasters

Rizk

Nishimur

Yamaguchi

et al. 2021

IJERPH

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Japan was hit by typhoon Hagibis, which came with torrential rains submerging almost eight-thousand buildings. For fast alleviation of and recovery from flood damage, a quick, broad, and accurate assessment of the damage situation is required. Image analysis provides a much more feasible alternative than on-site sensors due to their installation and maintenance costs. Nevertheless, most state-of-art research relies on only ground-level images that are inevitably limited in their field of vision. This paper presents a water level detection system based on aerial drone-based image recognition. The system applies the R-CNN learning model together with a novel labeling method on the reference objects, including houses and cars. The proposed system tackles the challenges of the limited and wild data set of flood images from the top view with data augmentation and transfer-learning overlaying Mask R-CNN for the object recognition model. Additionally, the VGG16 network is employed for water level detection purposes. We evaluated the proposed system on realistic images captured at disaster time. Preliminary results show that the system can achieve a detection accuracy of submerged objects of 73.42% with as low as only 21.43 cm error in estimating the water level.

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