شناسایی پایدار فعالیت فیزیکی انسان بر اساس سنسورهای گوشی هوشمند
محورهای موضوعی : مهندسی برق و کامپیوترمهدی یزدیان دهکردی 1 * , زهرا عابدی 2 , نسیم خانی 3
1 - دانشگاه یزد
2 - دانشگاه یزد
3 - دانشگاه یزد
کلید واژه: ژیروسکوپشتاب‌سنج شناسایی فعالیت فیزیکی انسان کیفیت سنسورگوشی هوشمندنویز سنسور,
چکیده مقاله :
در سال‌‌های اخیر تشخیص فعالیت فیزیکی انسان از روی داده‌های گرفته‌شده توسط سنسورهای ژیروسکوب و شتاب‌سنج در گوشی هوشمند، مورد توجه پژوهشگران قرار گرفته است. در این مقاله با به کارگیری روش تحلیل مؤلفه‌های اساسی، ویژگی‌هایی با بعد پایین و مناسب استخراج شده و کارایی چند طبقه‌بندی‌کننده مختلف شامل ماشین بردار پشتیبان، رگرسیون منطقی، ادابوست و شبکه عصبی کانولوشن برای طبقه‌بندی فعالیت‌ها بررسی و یک سیستم کارا برای این منظور پیشنهاد شده است. نتایج به دست آمده نشان می‌دهد که سیستم پیشنهادی توانسته است دقت تشخیص را نسبت به کارهای اخیر بهبود دهد. یکی از چالش‌هایی که لازم است در خصوص سیستم‌های تشخیص فعالیت مورد توجه قرار گیرد، میزان پایداری این سیستم‌ها نسبت به مدل‌های مختلف از گوشی‌های هوشمند است. با توجه به این که کیفیت سنسورها و نویز مرتبط با آنها از یک مدل گوشی به مدل دیگر متفاوت است، بنابراین بررسی میزان پایداری الگوریتم شناسایی فعالیت در نویزهای مختلف حایز اهمیت خواهد بود. در این مقاله کارایی و میزان پایداری طبقهبندیکنندهها در سطوح مختلف نویز نیز بررسی شده است. نتایج به دست آمده نشان می‌دهد که ماشین بردار پشتیبان با میانگین دقت 34/96% پایداری بهتری نسبت به نویز در مقایسه با سایر طبقهبندیکنندهها داشته است.
Human physical activity recognition using gyroscope and accelerometer sensors of smartphones has attracted many researches in recent years. In this paper, the performance of principle component analysis feature extraction method and several classifiers including support vector machine, logestic regression, Adaboost and convolutional neural network are evaluated to propose an efficient system for human activity recognition. The proposed system can improve the classification accuracy in comparison with the state of the art researches in this field. The performance of a physical activity recognition system is expected to be robust on different smartphone platforms. The quality of smartphone sensors and their corresponding noises vary considerably between different smartphone models and sometimes within the same model. Therefore, it is beneficial to study the effect of noise on the efficiency of the human activity recognition system. In this paper, the robustness of the investigated classifiers are also studied in various level of sensor noises to find the best robust solution for this purpose. The experimental results, which is provided on a well-known human activity recognition dataset, show that the support vector machine with averaged accuracy of 96.34% perform more robust than the other classifiers on different level of sensor noises.
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