Fall detection using acoustic features and one class classifiers

Title from PDF of title page (University of Missouri--Columbia, viewed on September 22, 2010).The entire thesis text is included in the research.pdf file; the official abstract appears in the short.pdf file; a non-technical public abstract appears in the public.pdf file.Thesis advisor: Dr. Mihail Popescu.M.S. University of Missouri--Columbia 2009.With the increasing of elderly population, there are more and more health problems occurring in everyday activities among this group of people. An investigation shows many elderly people get injures or trigger more serious health problems due to falling on the floor at their home or hospitals without artificial monitoring. There are many techniques to monitor the fall remotely and provide assistance as soon as possible. For this purpose video cameras are deployed at the place of living of an elderly but his might lead to an uncomfortable feeling of being spied on, hence we try to use just the sound (mainly frequency domain features) instead of video to detect a fall remotely. Sound signal is collected for a falling person along with normal everyday sounds, a classifier is trained using these sounds and using these classifiers we try to do the classification of an unknown sound as fall or non-fall. The next problem though is how to collect exact sound sample of a falling person as that is the first thing we want to avoid. In this work therefore we try to train our classifier using data from only one of the two classes which is the sound samples of normal everyday sounds only. These classifiers are called one class classifiers. We compare the performance of these classifiers with the conventional two class classifiers which uses examples from both the classes by testing both on the same dataset. Acoustic feature that we use to do classification is Mel Frequency Cepstrum coefficients or MFCC in short. We also test other spectrum based features like Energy Ratio Sub-band, Band-width and centroid Frequency.Includes bibliographical references

Ecosystem service enhancement for the alleviation of wildlife-human conflicts in the Aravalli Hills, Rajasthan, India

© 2017 Elsevier B.V. Conflict between people and ecosystem capacity is a global problem, and achievement of wildlife-human co-existence a strategic global need. Apex predators suffer disproportionately, including conflicts with human activities. Recovery of formerly declining predator populations, particularly India's Bengal tiger (Panthera tigris tigris), increases potential human conflict. Habitat conversion for arable production and proliferation of non-native tree species increases likelihood of conflict between wildlife, people and stock in villages in the Amlidha buffer zone between core areas of the Ranthambhore Tiger Reserve. Arresting and reversing landscape conversion in targeted zones can reduce potential wildlife-human conflict by regenerating ecosystem capacity, enabling coexistence of a ‘green corridor’ for terrestrial wildlife migration, a ‘blue corridor’ for movement of riverine wildlife, and sustainable human livelihoods. This can be achieved through informed and consensual community-based zoning of land uses, management of non-native species and regeneration of local water resources. Conversely, continuing habitat simplification will decrease ecosystem vitality and services, increasing wildlife-human conflict and insecurities. Transition to multifunctional ecosystem management doesn't require wholesale change; elective, consensual adjustments can enhance socio-ecological security. Initiatives by the NGO Tiger Watch involving village people, whose willing engagement is essential for sustainable management, support potential achievement of simultaneous wildlife conservation and human benefits

Melissopalynological, physicochemical and antioxidant properties of honey from West Coast of Malaysia

Stingless bees are native to tropical region and produce honey which are high in moisture content. Compared to honey from honeybees, there are limited studies on honey derived from stingless bees. Hence, the aim of this study was to evaluate the chemical composition and antioxidant activities of stingless bee honey. Fifteen types of honey were collected from six states in West Coast of Malaysia and pollen analyses were carried out. Four types of unifloral honey samples produced by stingless bees were selected to determine their physicochemical and antioxidant activities including total phenolic, total flavonoid and ascorbic acid contents. Melissopalynological study of 15 honey samples collected from different states showed presence of both unifloral and multifloral origins. Honey samples collected from Apis mellifera (honeybee) combs had lower number of total pollen compared to samples collected from Heterotrigona itama and Geniotrigona thoracica (stingless bees). Jambul Merak honey contains the highest phenolic and flavonoid contents with greatest color intensity and has the highest antioxidant potential. This study highlights the chemical composition and biological activity of honey from stingless bees which may increase its commercial value or to be utilised as potential functional food ingredient

FUMIL-Fuzzy Multiple Instance Learning for early illness recognition in older adults

Abstract — Many important applications in Health Sciences and Biology have underlying datasets that have ambiguous class membership, that is, individual labels are difficult to establish. In such cases, many times, the training examples are easier to label as a group rather than at the instance level. Multiple Instance Learning (MIL) is a supervised learning strategy that addresses this labeling difficulty by employing training example given as positive and negative bags of instances. In this paper we describe a fuzzy variation of the MIL Diverse Density framework (FUMIL) based on ordered weighted geometric operator (OWG) and fuzzy complement operators. We apply FUMIL for early illness recognition of elderly living alone in their home. The available data consists of wireless non-wearable sensor values aggregated at hour level (instance) and ground truth (medical data) available at day level (bag). In our preliminary experiments FUMIL performed better than the traditional MIL framework. Keywords- multiple instance learning;Fuzzy operators; eldercare;pattern recognition I