Multi Time Steps Prediction dengan Recurrent Neural Network Long Short Term Memory

Tidak tersedianya sumber daya alam seperti migas, hasil hutan ataupun industri manufaktur yang berskala besar di pulau Lombok menyebabkan pariwisata telah menjadi sektor andalan dalam pembangunan daerah. Kontribusi sektor pariwisata menunjukkan trend yang semakin meningkat dari tahun ke tahun. Dampak positif pengeluaran wisatawan terhadap perekonomian terdistribusikan ke berbagai sektor. Akan tetapi, pemerinatah daerah umumnya akan melakukan persiapan wisata daerah hanya pada saat even  lokal saja. Padahal kunjungan wisatawan bukan hanya karena faktor adanya event lokal saja. Persiapan pemerintah daerah dan pelaku wisata sangat penting untuk meningkatkan stabilitas kunjungan wisatawan. Penelitian ini mengkaji prediksi kunjungan wisatawan dengan pendekatan Recurrent Neural Network Long Short Term Memory (RNN LSTM). LSTM berisi informasi di luar aliran normal dari recurrent nertwork dalam gate cell. Cell membuat keputusan tentang apa yang harus disimpan dan kapan mengizinkan pembacaan, penulisan dan penghapusan, melalui gate yang terbuka dan tertutup. Gate menyampaikan informasi berdasarkan kekuatan yang masuk ke dalamnya dan akan difilter menjadi bobot dari gate itu sendiri. Bobot tersebut sama seperti bobot input dan hidden unit yang disesuaikan melalui proses leraning pada recurrent network. Hasil penelitian yang dilakukan dengan membangun model prediksi kunjungan wisatawan dengan RNN LSTM menggunakan multi time steps mendapatkan hasil RMSE sebesar 6888.37 pada data training dan 14684.33 pada data testing

Automated Intelligent Cueing Device to Improve Ambient Gait Behaviors for Patients with Parkinson\u27s Disease

Freezing of gait (FoG) is a common motor dysfunction in individuals with Parkinson’s disease (PD). FoG impairs walking and is associated with increased fall risk. Although pharmacological treatments have shown promise during ON-medication periods, FoG remains difficult to treat during medication OFF state and in advanced stages of the disease. External cueing therapy in the forms of visual, auditory, and vibrotactile, has been effective in treating gait deviations. Intelligent (or on-demand) cueing devices are novel systems that analyze gait patterns in real-time and activate cues only at moments when specific gait alterations are detected. In this study we developed methods to analyze gait signals collected through wearable sensors and accurately identify FoG episodes. We also investigated the potential of predicting the symptoms before their actual occurrence. We collected data from seven participants with PD using two Inertial Measurement Units (IMUs) on ankles. In our first study, we extracted engineered features from the signals and used machine learning (ML) methods to identify FoG episodes. We tested the performance of models using patient-dependent and patient-independent paradigms. The former models achieved 92.5% and 89.0% for average sensitivity and specificity, respectively. However, the conventional binary classification methods fail to accurately classify data if only data from normal gait periods are available. In order to identify FoG episodes in participants who did not freeze during data collection sessions, we developed a Deep Gait Anomaly Detector (DGAD) to identify anomalies (i.e., FoG) in the signals. DGAD was formed of convolutional layers and trained to automatically learn features from signals. The convolutional layers are followed by fully connected layers to reduce the dimensions of the features. A k-nearest neighbors (kNN) classifier is then used to classify the data as normal or FoG. The models identified 87.4% of FoG onsets, with 21.9% being predicted on average for each participant. This study demonstrates our algorithm\u27s potential for delivery of preventive cues. The DGAD algorithm was then implemented in an Android application to monitor gait patterns of PD patients in ambient environments. The phone triggered vibrotactile and auditory cues on a connected smartwatch if an FoG episode was identified. A 6-week in-home study showed the potentials for effective treatment of FoG severity in ambient environments using intelligent cueing devices