Development of a retrospective process for analyzing results of a HMM based posture recognition system in a functionalized nursing bed

In the area of care in general but especially in the care of elderly, there is a great interest in deriving patient parameters preparation free. For this purpose, a load cell functionalized nursing bed has been developed at Niederrhein University of Applied Science. The system allows analysis and recognition of the persons’ positions and actions in the bed. The Hidden Markov Toolkit (HTK) based posture recognition system was initially presented at the BMT 2015 by our research group. The initial system shows good results but to draw conclusions about the patient's condition, a minimum possible error rate should be achieved. For this purpose, a two-step retrospective analysis of the initial results was developed as an extension to improve the accuracy of the system

An approach to privacy-aware image analysis on edge devices using CNNs

This paper presents our approach to a privacy preserving person detection algorithm on edge computing devices. It utilizes a commonly used neural network architecture (VGG16) to encode an image by passing it through a fixed number of layers of the network on the edge device. The resulting feature vector can then be transmitted over the network to a more powerful computer (e.g. in the cloud) to be passed through the remaining layers of VGG16. As a result, the transmitted feature vector is only an abstract representation of the image. However, our research has shown that it is in fact possible to reconstruct most images by their respecting feature vectors if a potential attacker can tap into the network transmission by using a decoder network. Our approach to mitigate that risk is to preprocess the transmitted feature vector randomly by different manipulation methods. We used methods like mean filter or random null value insertion to manipulate the feature vector before it gets transmitted and show that those methods are able to counter the reconstruction capabilities of a decoder network while still preserving the capabilities of the original VGG16 network. The actual VGG16 network was used for object detection.We used the faces in the wild dataset and utilized two different approaches to confirm our approach. First, we used the VGGFace-Network on the decoded images and tried to let it identify people from the former mentioned dataset. The second approach used a group of 26 participants who had to match the decoder image to one of five images. In our experiments, we found different combinations of manipulation and number of layers on the edge device to preserve the detection capability of VGG16 (e.g. object detection) while preventing VGGFace and the participating group from identifying the shown people

Visualization of biomechanical model parameters by adapting methods from game development

In biosignal processing, the importance of modeling and simulation to assist the development of methods, algorithms and systems is increasing. However, models and simulation environments may also be used in the later application, e.g. to enable physicians, therapists and nurses to provide assistance in treatment. Most of the time, however, the difficulty is to implement and apply the rather complex model data or to evaluate the output of the model. For this purpose, the paper presents a method that intelligibly prepares the input data of a biomechanical model for the end user using a game development environmen

Bipartitioning and Encoding in Low-Power Pipelined Circuits

In this article, we present a bipartition dual-encoding architecture for low-power pipelined circuits. We exploit the bipartition approach as well as encoding techniques to reduce power dissipation not only of combinational logic blocks but also of the pipeline registers. Based on Shannon expansion, we partition a given circuit into two subcircuits such that the number of different outputs of both subcircuits are reduced, and then encode the output of both subcircuits to minimize the Hamming distance for transitions with a high switching probability. We measure the benefits of four different combinational bipartitioning and encoding architectures for comparison. The transistor-level simulation results show that bipartition dual-encoding can effectively reduce power by 72.7 % for the pipeline registers and 27.1 % for the total power consumption on average. To the best of our knowledge, it is the first work that presents an in-depth study on bipartition and encoding techniques to optimize power for pipelined circuits