@artikel{Hasan201755,
tajuk = {Analisis daya tindak balas tanah tiga dimensi semasa berjalan pada kanak-kanak dengan gangguan spektrum autisme},
pengarang = {C Z C Hasan and R Jailani and N Md Tahir and S Ilias},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85015640386&doi = 10.1016% 2fj.ridd.2017.02.015&rakan kongsi = 40&md5=d6a9839cda7f62bcce9bdcca33d3d33b},
doi = {10.1016/j.ridd.2017.02.015},
terbitan = {08914222},
tahun = {2017},
tarikh = {2017-01-01},
jurnal = {Penyelidikan Ketidakupayaan Pembangunan},
isi padu = {66},
halaman = {55-63},
penerbit = {Elsevier Inc.},
abstrak = {Maklumat minimum diketahui tentang tiga dimensi (3D) daya tindak balas tanah (GRF) mengenai corak gaya berjalan individu yang mengalami gangguan spektrum autisme (ASD). Tujuan kajian ini adalah untuk menyiasat sama ada komponen GRF 3D berbeza secara signifikan antara kanak-kanak dengan ASD dan kawalan rakan sebaya.. 15 kanak-kanak dengan ASD dan 25 biasanya berkembang (TD) kanak-kanak telah mengambil bahagian dalam kajian ini. Dua plat daya digunakan untuk mengukur data GRF 3D semasa berjalan. Teknik parameterisasi siri masa telah digunakan untuk mengekstrak 17 ciri diskret daripada bentuk gelombang GRF 3D. Dengan menggunakan ujian-t bebas dan ujian Mann-Whitney U, perbezaan yang ketara (hlm < 0.05) between the ASD and TD groups were found for four GRF features. Children with ASD demonstrated higher maximum braking force, lower relative time to maximum braking force, and lower relative time to zero force during mid-stance. Children with ASD were also found to have reduced the second peak of vertical GRF in the terminal stance. These major findings suggest that children with ASD experience significant difficulties in supporting their body weight and endure gait instability during the stance phase. The findings of this research are useful to both clinicians and parents who wish to provide these children with appropriate treatments and rehabilitation programs. © 2017 Elsevier Ltd},
nota = {dipetik oleh 8},
kata kunci = {Taburan Umur, Artikel, Autisme, Gangguan Spektrum Autisme, Fenomena Biomekanikal, Biomekanik, Keseimbangan Badan, Tinggi Badan, Berat badan, Berat badan, Anak-anak, Artikel Klinikal, Kajian Terkawal, Penilaian Penyakit, Perempuan, Langkah, Analisis Gait, Gangguan Gaya Berjalan, Angkatan Tindak Balas Tanah, Manusia, Pengimejan, Panjang Kaki, Malaysia, Lelaki, Pemeriksaan Neurologi, Patofisiologi, Fisiologi, Keseimbangan Postur, Prosedur, Psikologi, Statistik, Tiga Dimensi, Pengimejan Tiga Dimensi, Daya Tindak Balas Tanah Tiga Dimensi, berjalan},
pubstate = {diterbitkan},
tppubtype = {artikel}
}

@artikel{Hasan201774,
tajuk = {Use of statistical approaches and artificial neural networks to identify gait deviations in children with autism spectrum disorder},
pengarang = {C Z C Hasan and R Jailani and N Md Tahir},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85043500605&rakan kongsi = 40&md5=6f2ffe7c2f5daf9fd02d4456acb94438},
terbitan = {19984510},
tahun = {2017},
tarikh = {2017-01-01},
jurnal = {International Journal of Biology and Biomedical Engineering},
isi padu = {11},
halaman = {74-79},
penerbit = {North Atlantic University Union NAUN},
abstrak = {Automated differentiation of ASD gait from normal gait patterns is important for early diagnosis as well as ensuring rapid quantitative clinical decision and appropriate treatment planning. This study explores the use of statistical feature selection approaches and artificial neural networks (ANN) for automated identification of gait deviations in children with ASD, on the basis of dominant gait features derived from the three-dimensional (3D) joint kinematic data. The gait data from 30 ASD children and 30 normal healthy children were measured using a state-of-the-art 3D motion analysis system during self-selected speed barefoot walking. Kinematic gait features from the sagittal, frontal and transverse joint angles waveforms at the pelvis, hip, knee, and ankle were extracted using time-series parameterization. Two statistical feature selection techniques, namely the between-group tests (independent samples t-test and Mann-Whitney U test) and the stepwise discriminant analysis (SWDA) were adopted as feature selector to select the meaningful gait features that were then used to train the ANN. The 10-fold cross-validation test results indicate that the selected gait features using SWDA technique are more reliable for ASD gait classification with 91.7% ketepatan, 93.3% kepekaan, dan 90.0% kekhususan. The findings of the current study demonstrate that kinematic gait features with the combination of SWDA feature selector and ANN classifier would serve as a potential tool for early diagnosis of gait deviations in children with ASD as well as provide support to clinicians and therapists for making objective, accurate, and rapid clinical decisions that lead to the appropriate targeted treatments. © 2017 North Atlantic University Union NAUN. Hak cipta terpelihara.},
nota = {dipetik oleh 1},
kata kunci = {Artikel, Artificial Neural Network, Autisme, Tinggi Badan, Berat badan, Anak-anak, Artikel Klinikal, Kajian Terkawal, Analisis Diskriminan, Early Diagnosis, Perempuan, Langkah, Analisis Gait, Gangguan Gaya Berjalan, Manusia, Belajar, Lelaki, Pediatrics, Budak sekolah, Statistical Analysis, Statistik, Time Series Analysis},
pubstate = {diterbitkan},
tppubtype = {artikel}
}