Recognising human activity in free-living using multiple body-worn accelerometers

Objectives: Recognising human activity is very useful for an investigator about a patient's behaviour and can aid in prescribing activity in future recommendations. The use of body worn accelerometers has been demonstrated to be an accurate measure of human activity, however research looking at the use of multiple body worn accelerometers in a free living environment to recognise a wide range of activities is not evident. This study aimed to successfully recognise activity and sub-category activity types through the use of multiple body worn accelerometers in a free living environment. Method: Ten participants (Age = 23.1 ± 1.7 years, height =171.0 ± 4.7 cm, mass = 78.2 ± 12.5 Kg) wore nine body-worn accelerometers for a day of free living. Activity type was identified through the use of a wearable camera, and sub category activities were quantified through a combination of free-living and controlled testing. A variety of machine learning techniques consisting of pre-processing algorithms, feature and classifier selections were tested, accuracy and computing time were reported. Results: A fine k-nearest neighbour classifier with mean and standard deviation features of unfiltered data reported a recognition accuracy of 97.6%. Controlled and free-living testing provided highly accurate recognition for sub-category activities (>95.0%). Decision tree classifiers and maximum features demonstrated to have the lowest computing time. Conclusions: Results show recognition of activity and sub-category activity types is possible in a free living environment through the use of multiple body worn accelerometers. This method can aid in prescribing recommendations for activity and sedentary periods for healthy living

Audio, Text, Image, and Video Digital Watermarking Techniques for Security of Media Digital

The proliferation of multimedia content as digital media assets, encompassing audio, text, images, and video, has led to increased risks of unauthorized usage and copyright infringement. Online piracy serves as a prominent example of such misuse. To address these challenges, watermarking techniques have been developed to protect the copyright of digital media while maintaining the integrity of the underlying content. Key characteristics evaluated in watermarking methods include capability, privacy, toughness, and invisibility, with robustness playing a crucial role. This paper presents a comparative analysis of digital watermarking methods, highlighting the superior security and effective watermark image recovery offered by singular value decomposition. The research community has shown significant interest in watermarking, resulting in the development of various methods in both the spatial and transform domains. Transform domain approaches such as Discrete Cosine Transform, Discrete Wavelet Transform, and Singular Value Decomposition, along with their interconnections, have been explored to enhance the effectiveness of digital watermarking methods

Note: An object detection method for active camera

To solve the problems caused by a changing background during object detection in active camera, this paper proposes a new method based on SURF (speeded up robust features) and data clustering. The SURF feature points of each image are extracted, and each cluster center is calculated by processing the data clustering of k adjacent frames. Templates for each class are obtained by calculating the histograms within the regions around the center points of the clustering classes. The window of the moving object can be located by finding the region that satisfies the histogram matching result between adjacent frames. Experimental results demonstrate that the proposed method can improve the effectiveness of object detection.Yong Chen, Ronghua Zhang, Lei Shang, and Eric H

Detecting Alzheimer\u27s Disease using Artificial Neural Networks

This project aims to use artificial neural networks (ANN) in order to detect Alzheimer’s disease. More specifically, convolutional neural networks (CNN) will be utilized as this is the most common ANN and has been used in many different image processing applications. The purpose of using artificial neural networks as a detect method is so that an intelligent way for image and signal analysis can be used. A software that implements CNN will be developed so that users in medical settings can utilize this software to detect Alzheimer’s in patients. The input for this software will be the patient’s MRI scans. In addition, this is a project that is relevant with the current trends of an increase in development surrounding artificial intelligence. As technology has become more advanced, there has been an increase in medical developments as well. From the simulation, the hyperbolic tangent activation function provided the best results. The performance resulting from the two classifications when using the hyperbolic tangent function, on average was validation best accuracy of 81.10%, validation stopped tuning accuracy of 81.10%, training best accuracy of 100.00%, testing best accuracy of 68.94%, F-1 score of 70.06%, precision of 71.00%, and recall of 70.06%. This project will open doors to more applications of this detection method. More diseases other than Alzheimer’s disease can utilize artificial neural networks (ANN) to detect diseases early on so that lives can be restored and saved

A Lossy Colour Image Compression Using Integer Wavelet Transforms and Binary Plane Transform

In the recent period, image data compression is the major component of communication and storage systems where the uncompressed images requires considerable compression technique, which should be capable to reduce the crippling disadvantages of data transmission and image storage. In the research paper, the novel image compression technique is proposed which is based on the spatial domain and quite effective for the compression of images. However, the performance of the proposed methodology is compared with the conventional compression techniques (Joint Photographic Experts Group) JPEG and set partitioning in hierarchical trees (SPIHT) using the evaluation metrics compression ratio and peak signal to noise ratio. It is evaluated that Integer wavelets with binary plane technique is more effective compression technique than JPEG and SPIHT as it provided more efficient quality metrics values and visual quality

A Good Performance OTP Encryption Image based on DCT-DWT Steganography

The security aspect is very important in data transmission. One way to secure data is with steganography and cryptography. Surely research on this should continue to be developed to improve security. In this paper, we proposed a combination of steganographic and cryptographic algorithms for double protection during data transmission. The selected steganographic algorithm is the use of a combination of DCT and DWT domain transformations. Because the Imperceptibility aspect is a very important aspect of steganographic techniques, this aspect needs to be greatly improved. In the proposed method of DCT transformation first, proceed with DWT transformation. From the experimental results obtained better imperceptibility quality, compared with existing methods. To add OTP message security applied algorithm to encrypt the message image, before it is inserted. This is evidenced by experiments conducted on 20 grayscale images measuring 512x512 with performance tests using MSE, PSNR, and NC. Experimental results prove that DCT-DWT-OTP generates PNSR more than 50 dB, and NC of all images is 1