Transportation mode recognition fusing wearable motion, sound and vision sensors

We present the first work that investigates the potential of improving the performance of transportation mode recognition through fusing multimodal data from wearable sensors: motion, sound and vision. We first train three independent deep neural network (DNN) classifiers, which work with the three types of sensors, respectively. We then propose two schemes that fuse the classification results from the three mono-modal classifiers. The first scheme makes an ensemble decision with fixed rules including Sum, Product, Majority Voting, and Borda Count. The second scheme is an adaptive fuser built as another classifier (including Naive Bayes, Decision Tree, Random Forest and Neural Network) that learns enhanced predictions by combining the outputs from the three mono-modal classifiers. We verify the advantage of the proposed method with the state-of-the-art Sussex-Huawei Locomotion and Transportation (SHL) dataset recognizing the eight transportation activities: Still, Walk, Run, Bike, Bus, Car, Train and Subway. We achieve F1 scores of 79.4%, 82.1% and 72.8% with the mono-modal motion, sound and vision classifiers, respectively. The F1 score is remarkably improved to 94.5% and 95.5% by the two data fusion schemes, respectively. The recognition performance can be further improved with a post-processing scheme that exploits the temporal continuity of transportation. When assessing generalization of the model to unseen data, we show that while performance is reduced - as expected - for each individual classifier, the benefits of fusion are retained with performance improved by 15 percentage points. Besides the actual performance increase, this work, most importantly, opens up the possibility for dynamically fusing modalities to achieve distinct power-performance trade-off at run time

Tre Claverstykker

Set of three pieces for piano (1. Berceuse, 2. Juleklokker, and 3. Ungarsk Dans). Front cover signed by Krohn in the upper right hand corner. Plate numbers 1, 2, 3.https://scholarexchange.furman.edu/krohn-album2/1003/thumbnail.jp

In vitro Toxicity Testing in the Twenty-First Century

The National Research Council (NRC) article “Toxicity Testing in the 21st Century: A vision and A Strategy” (National Research Council, 2007) was written to bring attention to the application of scientific advances for use in toxicity tests so that chemicals can be tested in a more time and cost efficient manner while providing a more relevant and mechanistic insight into the toxic potential of a compound. Development of tools for in vitro toxicity testing constitutes an important activity of this vision and contributes to the provision of test systems as well as data that are essential for the development of computer modeling tools for, e.g., system biology, physiologically based modeling. This article intends to highlight some of the issues that have to be addressed in order to make in vitro toxicity testing a reality in the twenty-first century

Urban Planning and Cultural Identity in Pompeii: from the Altstadt to Vitruvius

This thesis addresses the urban planning of Pompeii’s Altstadt, or oldest settlement, and its place in a larger narrative on Mediterranean urbanism. The first phase of this research utilizes Stephen Marshall’s techniques to quantify the Altstadt’s urban form, and implements additional architectural analysis to uncover the original street network. These techniques reveal that the ancient city utilized traditional grid forms but was forced to accommodate for irregular terrain. Oriented regardless of topography, its layout has a similar rotation to Vitruvius’s ideal city even though it predated De Architectura by five centuries. This specific orientation connects Pompeii’s urban development to that of other archaic cities around the Mediterranean. Because Pompeii’s culture is best defined as a triangulation of Greek, Etruscan, and Oscan, this research complicates previous assumptions that Vitruvius’ intellectual precursor was wholly Greek and questions the scholarly tendency of attributing strict cultural boundaries to various ancient practices

Sound-based transportation mode recognition with smartphones

Smartphone-based identification of the mode of transportation of the user is important for context-aware services. We investigate the feasibility of recognizing the 8 most common modes of locomotion and transportation from the sound recorded by a smartphone carried by the user. We propose a convolutional neural network based recognition pipeline, which operates on the short- time Fourier transform (STFT) spectrogram of the sound in the log domain. Experiment with the Sussex-Huawei locomotion- transportation (SHL) dataset on 366 hours of data shows promising results where the proposed pipeline can recognize the activities Still, Walk, Run, Bike, Car, Bus, Train and Subway with a global accuracy of 86.6%, which is 23% higher than classical machine learning pipelines. It is shown that sound is particularly useful for distinguishing between various vehicle activities (e.g. Car vs Bus, Train vs Subway). This discriminablity is complementary to the widely used motion sensors, which are poor at distinguish between rail and road transport

Deep convolutional and LSTM recurrent neural networks for multimodal wearable activity recognition

Human activity recognition (HAR) tasks have traditionally been solved using engineered features obtained by heuristic processes. Current research suggests that deep convolutional neural networks are suited to automate feature extraction from raw sensor inputs. However, human activities are made of complex sequences of motor movements, and capturing this temporal dynamics is fundamental for successful HAR. Based on the recent success of recurrent neural networks for time series domains, we propose a generic deep framework for activity recognition based on convolutional and LSTM recurrent units, which: (i) is suitable for multimodal wearable sensors; (ii) can perform sensor fusion naturally; (iii) does not require expert knowledge in designing features; and (iv) explicitly models the temporal dynamics of feature activations. We evaluate our framework on two datasets, one of which has been used in a public activity recognition challenge. Our results show that our framework outperforms competing deep non-recurrent networks on the challenge dataset by 4% on average; outperforming some of the previous reported results by up to 9%. Our results show that the framework can be applied to homogeneous sensor modalities, but can also fuse multimodal sensors to improve performance. We characterise key architectural hyperparameters’ influence on performance to provide insights about their optimisation

Evolutionary morphogenesis for multi-cellular systems

With a gene required for each phenotypic trait, direct genetic encodings may show poor scalability to increasing phenotype length. Developmental systems may alleviate this problem by providing more efficient indirect genotype to phenotype mappings. A novel classification of multi-cellular developmental systems in evolvable hardware is introduced. It shows a category of developmental systems that up to now has rarely been explored. We argue that this category is where most of the benefits of developmental systems lie (e.g. speed, scalability, robustness, inter-cellular and environmental interactions that allow fault-tolerance or adaptivity). This article describes a very simple genetic encoding and developmental system designed for multi-cellular circuits that belongs to this category. We refer to it as the morphogenetic system. The morphogenetic system is inspired by gene expression and cellular differentiation. It focuses on low computational requirements which allows fast execution and a compact hardware implementation. The morphogenetic system shows better scalability compared to a direct genetic encoding in the evolution of structures of differentiated cells, and its dynamics provides fault-tolerance up to high fault rates. It outperforms a direct genetic encoding when evolving spiking neural networks for pattern recognition and robot navigation. The results obtained with the morphogenetic system indicate that this "minimalist” approach to developmental systems merits further stud

BlueSense: designing an extensible platform for wearable motion sensing, sensor research and IoT applications

We present an extensible sensor research platform for wearable and IoT applications. The result is a 30x30mm platform capable of 500Hz motion and orientation sensing using 98mW when logging the data. The platform can wake up at programmed intervals using only 70uW in hardware off mode. A maximum 0.6ppm time deviation between nodes allows usage in a network for whole body movement sensing