Pengecaman peristiwa jatuh secara tiba-tiba menggunakan fitur gerakan dan pengelas ilhaman biologi sistem penglihatan

Kajian tentang pengecaman peristiwa yang berlaku secara tiba-tiba untuk sistem video pengawasan dikenal pasti boleh menyumbang ke arah pengurangan kos pembangunan teknologi sistem peranti pengesan bolehpakai dan juga ketidakselesaan pemakainya. Adalah dijangkakan, populasi penduduk dunia akan bertambah pada masa akan datang ekoran peningkatan jangka hayat manusia yang menyebabkan peningkatan bilangan penduduk dunia berumur 60 tahun ke atas. Oleh itu, sistem penjagaan keselamatan penghuni dalam rumah tak invasif yang boleh berfungsi untuk mengawas dan mengesan sebarang kejadian kemalangan yang tidak diingini seperti rebah, pengsan dan lain-lain akan menjadi penting dan berguna untuk warga tua khususnya untuk mereka yang tinggal bersendirian. Perkembangan dalam sistem pengecaman peristiwa yang berlaku secara tiba-tiba dijangkakan dapat menyediakan kemudahan kepada warga tua yang tinggal bersendirian di samping berupaya menjaga keselamatan mereka di rumah. Ini akan dapat mengurangkan kos perbelanjaan di pusat jagaan warga tua. Justeru, objektif utama kajian adalah untuk membangunkan satu kaedah mengesan gerakan dan mengecam peristiwa yang berlaku secara tiba-tiba dan memerlukan tindakan serta perhatian segera. Perlaksanaan pembangunan kaedah pengecaman kejadian melibatkan tiga langkah penting iaitu, pemprosesan awal, penyarian fitur dan pengelasan. Pemprosesan awal menggunakan teknik penolakan latar belakang (PLB) dan teknik pelicinan, (penuras kebarangkalian ruang, SPF dan sokongan data kejiranan, NDS) untuk mengurangkan hingar imej bebayang objek. Sifat gerakan telah dikenalpasti sebagai salah satu sifat yang penting dan relevan bagi mengesan perubahan mendadak pada orientasi, arah dan penampilan objek dalam sesebuah jujukan video. Terdapat tiga kaedah sarian fitur gerakan yang berasaskan ruang-masa iaitu templat, aliran vektor gerakan (AVG) dan ilhaman biologi sistem penglihatan manusia telah dilaksanakan. Seterusnya, keberkesanan fitur gerakan diuji dengan menggunakan tiga pengelas sedia ada iaitu k-kejiranan terdekat (k-NN), mesin vektor sokongan (SVM) dan rangkaian neural inspirasi biologi suap hadapan (BFFNN-P). Potensi pengelas BFFNN-P untuk mengelas peristiwa jatuh berbanding dengan aktiviti harian yang lain ditingkatkan melalui kaedah kawalan ralat berkadar (P), kamiran (I) dan terbitan (D). Hasil kajian yang diperolehi menunjukkan teknik SPF telah memberikan keputusan yang baik dalam mengurangkan hingar dan melicinkan imej bebayang objek. Fitur gerakan GaussH yang berasaskan inspirasi sistem penglihatan manusia telah memberikan keputusan yang lebih baik berbanding templat dan AVG dengan menggunakan pengelas BFFNN-PD. Prestasi kejituan, kepekaan dan kepekaan bagi fitur gerakan GaussH dengan pengelas BFFNN-PD adalah 98.6%, 98.2% dan 99.5%. Kesimpulannya, penyelidikan ini telah berjaya menghasilkan kaedah pengelasan melalui pendekatan inspirasi biologi yang mampu mengesan peristiwa yang berlaku secara tiba-tiba

Kinect-based physiotherapy and assessment: a comprehensive review

In this paper, we discuss a review of the present Kinect-based physiotherapy and assessment for rehabilitation patients to provide an outline of the state of art, limitation and issues of concern as well as suggestion for future work in this approach. The paper is constructed into three main parts, each part presenting a review for a particular topic. The introduction was discussed on physiotherapy exercises and the limitation of current Kinect-based applications. Next, we also discuss on Kinect Skeleton Joint and Kinect Depth Map features that being used widely nowadays. A concise summary with significant findings of each paper had been tabulate for each feature; Skeleton Joints and Depth Map. Afterward we assemble a quite number of classification method that being implemented for activity recognition in past few years

Semantic object detection for human activity monitoring system

Semantic object detection is significant for activity monitoring system. Any abnormalities occurred in a monitored area can be detected by applying semantic object detection that determines any displaced objects in the monitored area. Many approaches are being made nowadays towards better semantic object detection methods, but the approaches are either resource consuming such as using sensors that are costly or restricted to certain scenarios and background only. We assume that the scale structures and velocity can be estimated to define a different state of activity. This project proposes Histogram of Oriented Gradient (HOG) technique to extract feature points of semantic objects in the monitored area while Histogram of Oriented Optical Flow (HOOF) technique is used to annotate the current state of the semantic object that having human-and-object interaction. Both passive and active objects are extracted using HOG, and HOOF descriptor indicate the time series status of the spatial and orientation of the semantic object. Support Vector Machine technique uses the predictors to train and test the input video and classify the processed dataset to its respective activity class. We evaluate our approach to recognise human actions in several scenarios and achieve 89% accuracy with 11.3% error rate

Optimal accelerometer placement for fall detection of rehabilitation patients

The development of health monitoring system using wearable sensor has lots of potential in the field of rehabilitation and gained lots of attention in the scientific community and industry. The aim and motivation in this field are to focus on the application of wearable technology to monitor elderly or rehab patients in home-based settings to reduce resources and development cost. The wearable sensor such as accelerometer used to emphasise the clinical applications of fall detection during rehabilitation treatment. This paper is intended to determine the optimal sensor placement especially for lower limb activity during rehabilitation exercise. Accelerometer data were collected from three different body locations (hip, thigh, and foot). The lower limb activities involve normal movements such as walking, lifting, sit-to-stand, and stairs. Other unexpected activity such as falls might occur during normal lower limb exercise movement. Then, acceleration data for various lower limbs activities was classified using k-NN and SVM classifier. The result found that the hip was the best location to record data for lower limb activities including when fall occurs

Spiking neural network classification for spike train analysis of physiotherapy movements

Classifying gesture or movements nowadays become a demanding business as the technologies of sensor rose. This has enchanted many researchers to actively investigated widely within the area of computer vision. Rehabilitation exercises is one of the most popular gestures or movements that being worked by the researchers nowadays. Rehab session usually involves experts that monitored the patients but lacking the experts itself made the session become longer and unproductive. This works adopted a dataset from UI-PRMD that assembled from 10 rehabilitation movements. The data has been encoded into spike trains for spike patterns analysis. Next, we tend to train the spike trains into Spiking Neural Networks and resulting into a promising result. However, in future, this method will be tested with other data to validate the performance, also to enhance the success rate of the accuracy

Optimal Accelerometer Placement for Fall Detection of Rehabilitation Patients

The development of health monitoring system using wearable sensor has lots of potential in the field of rehabilitation and gained lots of attention in the scientific community and industry. The aim and motivation in this field are to focus on the application of wearable technology to monitor elderly or rehab patients in home-based settings to reduce resources and development cost. The wearable sensor such as accelerometer used to emphasise the clinical applications of fall detection during rehabilitation treatment. This paper is intended to determine the optimal sensor placement especially for lower limb activity during rehabilitation exercise. Accelerometer data were collected from three different body locations (hip, thigh, and foot). The lower limb activities involve normal movements such as walking, lifting, sit-to-stand, and stairs. Other unexpected activity such as falls might occur during normal lower limb exercise movement. Then, acceleration data for various lower limbs activities was classified using k-NN and SVM classifier. The result found that the hip was the best location to record data for lower limb activities including when fall occurs

Glaucoma detection of retinal images based on boundary segmentation

The rapid growth of technology makes it possible to implement in immediate diagnosis for patients using image processing. By using morphological processing and adaptive thresholding method for segmentation of optic disc and optic cup, various sizes of retinal fundus images captured through fundus camera from online databases can be processed. This paper explains the use of color channel separation method for pre-processing to remove noise for better optic disc and optic cup segmentation. Noise removal will improve image quality and in return help to increase segmentation standard. Then, morphological processing and adaptive thresholding method is used to extract out optic disc and optic cup from fundus image. The proposed method is tested on two publicly available online databases: RIM-ONE and DRIONS-DB. On RIM-ONE database, the average PSNR value acquired is 0.01891 and MSE is 65.62625. Meanwhile, for DRIONS-DB database, the best PSNR is 64.0928 and the MSE is 0.02647. In conclusion, the proposed method can successfully filter out any unwanted noise in the image and are able to help clearer optic disc and optic cup segmentation to be performed

Semantic object detection for human activity monitoring system

Semantic object detection is significant for activity monitoring system. Any abnormalities occurred in a monitored area can be detected by applying semantic object detection that determines any displaced objects in the monitored area. Many approaches are being made nowadays towards better semantic object detection methods, but the approaches are either resource consuming such as using sensors that are costly or restricted to certain scenarios and background only. We assume that the scale structures and velocity can be estimated to define a different state of activity. This project proposes Histogram of Oriented Gradient (HOG) technique to extract feature points of semantic objects in the monitored area while Histogram of Oriented Optical Flow (HOOF) technique is used to annotate the current state of the semantic object that having human-and-object interaction. Both passive and active objects are extracted using HOG, and HOOF descriptor indicate the time series status of the spatial and orientation of the semantic object. Support Vector Machine technique uses the predictors to train and test the input video and classify the processed dataset to its respective activity class. We evaluate our approach to recognize human actions in several scenarios and achieve 89% accuracy with 11.3% error rate