Rehabilitation is a crucial process for patients suffering from motor disorders. The current practice is performing rehabilitation exercises under clinical expert supervision. New approaches are needed to allow patien...
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Rehabilitation is a crucial process for patients suffering from motor disorders. The current practice is performing rehabilitation exercises under clinical expert supervision. New approaches are needed to allow patients to perform prescribed exercises at their homes and alleviate commuting requirements, expert shortages, and healthcare costs. Human joint estimation is a substantial component of these programs since it offers valuable visualization and feedback based on body movements. Camera-based systems have been popular for capturing joint motion. However, they have high-cost, raise serious privacy concerns, and require strict lighting and placement settings. We propose a millimeter-wave (mmWave)-based assistive rehabilitation system (MARS) for motor disorders to address these challenges. MARS provides a low-cost solution with a competitive object localization and detection accuracy. It first maps the 5D time-series point cloud from mmWave to a lower dimension. Then, it uses a convolution neural network (CNN) to estimate the accurate location of human joints. MARS can reconstruct 19 human joints and their skeleton from the point cloud generated by mmWave radar. We evaluate MARS using ten specific rehabilitation movements performed by four human subjects involving all body parts and obtain an average mean absolute error of 5.87 cm for all joint positions. To the best of our knowledge, this is the first rehabilitation movements dataset using mmWave point cloud. MARS is evaluated on the Nvidia Jetson Xavier-NX board. Model inference takes only 64 mu s and consumes 442 mu J energy. These results demonstrate the practicality of MARS on low-power edge devices.
Fast numerical libraries have been a cornerstone of scientific computing for decades, but this comes at a price. Programs may be tied to vendor specific software ecosystems resulting in polluted, non-portable code. As...
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ISBN:
(纸本)9781728136134
Fast numerical libraries have been a cornerstone of scientific computing for decades, but this comes at a price. Programs may be tied to vendor specific software ecosystems resulting in polluted, non-portable code. As we enter an era of heterogeneous computing, there is an explosion in the number of accelerator libraries required to harness specialized hardware. We need a system that allows developers to exploit ever-changing accelerator libraries, without over-specializing their *** we cannot know the behavior of future libraries ahead of time, this paper develops a scheme that assists developers in matching their code to new libraries, without requiring the source code for these ***, it can recover equivalent code from programs that use existing libraries and automatically port them to new interfaces. It first uses program synthesis to determine the meaning of a library, then maps the synthesized description into generalized constraints which are used to search the program for replacement opportunities to present to the *** applied this approach to existing large applications from the scientific computing and deep learning domains. Using our approach, we show speedups ranging from 1.1× to over 10× on end to end performance when using accelerator libraries.
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