IMPACT OF NUMBER OF ATTRIBUTES ON THE ACCURACY OF HUMAN MOTION CLASSIFICATION

The quality of the human motion data faces challenges in producing high classification accuracy in large data streams for essential knowledge discovery. This reflects the need to identify the key factors that affect the results of classification. Present studies merely focus on estimating joints, skeleton and motions of human activities. However, the effect of the number of attributes towards classification accuracies of human motion has not been discussed. Therefore, this paper is aimed at determining the amount of attributes that affect the qualities of human motion classification. The case studies involve simple locomotion activities: jumping, walking and running retrieved from the public available domain. The raw video data were transformed into numeric in the form of x and y-coordinates and rotation angles as to be tested from a single up to triple combinations of data attributes. The impact of the number of attributes on classification accuracy is evaluated via Bayes, Function, Lazy, Meta, Rule and Trees classifier algorithms supported by the WEKA tool. Results revealed that three attributes data gave the best classification performance with an average accuracy of 81.50%. The findings also revealed that the number of attribute is directly proportional to the classification accuracy of human motion data

Klasifikasi Aktivitas Gerakan Bayi Setelah Operasi Berbasis Motion Vector Menggunakan Support Vector Machine

Rasa sakit adalah pengalaman subjektif dan tidak ada tes objektif untuk mengukurnya, IASP (Asosiasi Internasional untuk Studi Rasa Sakit) memutuskan bahwa pasien menyampaikan rasa sakit yang dirasakan sebagai standar penilaian nyeri hingga saat ini. Sedangkan Bayi tidak bisa menyampaikan rasa sakit secara verbal. Dalam tulisan ini, kami mengembangkan sebuah sistem untuk mendeteksi aktivitas gerakan bayi setelah operasi dengan mengamati fitur vektor gerak berdasarkan FLACC. Pada FLACC, aktivitas adalah satu dari lima kategori untuk mengidentifikasi tingkat nyeri pada bayi dimana kategori aktivitas pada FLACC terbagi dalam 3 aktivitas, yaitu aktivitas gerakan tenang dengan skor 0, menggeliat memiliki skor 1 dan menyentak memiliki skor 2. Masukan sistem berupa video berukuran 640 x 480, dengan menggunakan algoritma block matching menggunakan sum of absolute difference dengan ukuran blok 8 x 8 dengan pendekatan deret Taylor untuk menghasilkan nilai dengan akurasi tinggi dari gerakan frame referensi ke frame tujuan saat ini dalam bentuk vektor gerak. Gerakan yang terjadi antar frame yang diamati, kemudian dilakukan pengurutan pada skala terbesar untuk mengetahui gerakan terbesar dan sekaligus bertindak sebagai fitur antar frame. Kemudian dilakukan klasifikasi menggunakan SVM (support vector machine) untuk mendapatkan kelas aktivitas berdasarkan FLACC yaitu Tenang, Menggeliat dan Menyentak. Hasil akurasi terbaik pada proses klasifikasi mencapai nilai 90,4762%. Metode yang diusulkan dalam penelitian ini adalah penelitian yang pertama pada klasifikasi aktivitas gerakan pada bayi setelah operasi. =============================================================================================== Pain is a subjective experience and no objective test exist to measure it, IASP (International Association for the Study of Pain) decided patients self-report as gold standard of pain assessment. Infant cannot provide a self-report of pain verbally. In this reseach, we developed a system to recognize post-surgery infant activity based FLACC (Face, Legs, Activity, Cry, Consolability), score 0 is given if the infant moves easily, score 1 if the infant is squirming, and a score 2 if the baby is jerking by observing the features of motion. In FLACC, activity is one of the five parameters to identify the level of infant pain. Using a block matching algorithm with the addition of the Taylor series to generate a value with high motion accuracy from the reference frame to the current frame in the form of a motion vector. Videos have been verified by doctors and nurses using hormone cortisol with FLACC measurements. The results of the experiment show the classification using SVM could detect the infant activity moves easily, squirming, and jerking at 90.4762%. Nevertheless, this experiment is still novel and needs further study on the infant activity

Efficient motion estimation methods for fast recognition of activities of daily living

This work proposes a framework for the efficient recognition of activities of daily living (ADLs), captured by static color cameras, applicable in real world scenarios. Our method reduces the computational cost of ADL recognition in both compressed and uncompressed domains by introducing system level improvements in State of-the-Art activity recognition methods. Faster motion estimation methods are employed to replace costly dense optical flow (OF) based motion estimation, through the use of fast block matching methods, as well as motion vectors, drawn directly from the compressed video domain (MPEG vectors). This results in increased computational efficiency, with minimal loss in terms of recognition accuracy. To prove the effectiveness of our approach, we provide an extensive, in-depthinvestigation of the trade-offs between computational cost, compression efficiency and recognition accuracy, tested on bench-mark and real-world ADL video datasets