Many prey species exhibit antipredator responses in the presence of humans. These responses may lead in turn to behavioral modification and spatiotemporal avoidance strategies that may have implications for long term population dynamics. Our research was developed to measure the potential effects of backcountry recreation on the behavior and distribution of desert bighorn sheep in the Pusch ridge Wilderness Area (PRWA), Arizona, USA. Human use of the PRWA was quantified across the study site using real time observer field counts and modeled use metrics derived from motion activated trail cameras (n=15) placed on six US Forest Service (USFS) trails. We conducted 113 behavioral observations at multiple spatial scales from February of 2015 through May of 2016 to quantify female bighorn activity budgets and responses to human interaction. Bighorn behavior was characterized in a generalized linear model (GLM) to examine how human use and environmental covariates affect changes in the frequency of behaviors within the bighorn activity budget. Our models indicate that interactions between bighorn and humans are complex. An increase in human activity in the PRWA correlates inversely with bighorn time spent grazing. As a potential trade off bighorn significantly increased the frequency of time bedded. These results suggest that bighorn behavioral responses to human activity may carry costs associated with avoidance, however, behavioral analysis alone is not enough to measure the extent of such costs. This research has management implications where multiple use and high levels of human activity have the potential to negatively influence the behavior of wildlife species.

Building smart home environments which automatically or semi-automatically assist and comfort occupants is an important topic in the pervasive computing field, especially with the coming of cheap, easy-to-install sensors. This has given rise to the indispensable need for human activity recognition from ubiquitous sensors whose purpose is to observe and understand what occupants are trying to do from sensory data. The main approach to the problem of human activity recognition is a probabilistic one so as to handle the complication of uncertainty, the overlapping of human behaviours and environmental noise. This thesis develops a probabilistic model as a framework for human activity recognition using multiple multi-modal sensors in complex pervasive environments. The probabilistic model to be developed is adapted and based on the abstract hidden Markov model (AHMM) with one layer to fuse multiple sensors. The concept of factored state representation is employed in the model to parsimoniously represent the state transitions for reducing the number of required parameters. The exact method is used in learning the model’s parameters and performing inference. To be able to incorporate a large number of sensors, several more parsimonious representations including the mixtures of smaller multinomials and sigmoid functions are investigated to model the state transitions, resulting in a reduction of the number of parameters and time required for training. / We examine the approximate variational method to significantly reduce the time required for training the model instead of using the exact method. A system of fixed point equations is derived to iteratively update the free variational parameters. We also present the factored model in the case where all variables are continuous with the use of the conditional Gaussian distribution to model state transitions. The variational method is still employed in this case to speed up the model’s training process. The developed model is implemented and applied in recognizing daily activity in our smart home and the Nokia lab from multiple sensors. The experimental results show that the model is appropriate for fusing multiple sensors in activity recognition with a reasonable recognition performance.

Upper Palaeolithic cave art research has tended to focus on the images themselves, rather than the physical and social circumstances of their production. This dissertation explores and develops new practice-based ways of investigating cave art. A method analysing features of the cave environment, such as light, space and accessibility, internal conditions etc., and how these relate to traces of human activity, is developed and applied to three decorated caves from Upper Palaeolithic in southern France: Marsoulas (Haute-Garonne), Chauvet (Ardèche) and Rouffignac (Périgord). New insights are suggested into the underlying practice of cave art and its significance in Upper Palaeolithic societies. / La recherche l’art rupestre Paléolithique supérieur a eu tendance à se focaliser sur les images elles-mêmes, plutôt que les circonstances physiques et sociales de leur production. Cette dissertation explore et développe des nouvelles formes d’investigation de l’art rupestre basées sur la pratique. Une méthode pour analyser des caractéristiques de l’environnement de la grotte, comme la lumière, l’espace et l’accessibilité, des conditions internes etc., et comment ceux-ci sont associés à des traces de l’activité humaine, est développée et appliquée à trois grottes de l’époque Paléolithique supérieur dans le sud de France: Marsoulas (Haute-Garonne), Chauvet (Ardèche) et Rouffignac (Périgord). Une nouvelle vision est suggérée pour la pratique sous-jacente de l’art rupestre et son importance dans les sociétés paléolithiques.

Thesis (Ph. D.)--Oregon State University, 2009. / Printout. Includes bibliographical references (leaves 129-138). Also available on the World Wide Web.

The elderly living in smart homes can have their daily movement recorded and analyzed. Given the fact that different elders can have their own living habits, a methodology that can automatically identify their daily activities and discover their daily routines will be useful for better elderly care and support. In this thesis research, we focus on developing data mining algorithms for automatic detection of behavioral patterns from the trajectory data of an individual for activity identification, daily routine discovery, and activity prediction. The key challenges for the human activity analysis include the need to consider longer-range dependency of the sensor triggering events for activity modeling and to capture the spatio-temporal variations of the behavioral patterns exhibited by human. We propose to represent the trajectory data using a behavior-aware flow graph which is a probabilistic finite state automaton with its nodes and edges attributed with some local behavior-aware features. Subflows can then be extracted from the flow graph using the kernel k-means as the underlying behavioral patterns for activity identification. Given the identified activities, we propose a novel nominal matrix factorization method under a Bayesian framework with Lasso to extract highly interpretable daily routines. To better take care of the variations of activity durations within each daily routine, we further extend the Bayesian framework with a Markov jump process as the prior to incorporate the shift-invariant property into the model. For empirical evaluation, the proposed methodologies have been compared with a number of existing activity identification and daily routine discovery methods based on both synthetic and publicly available real smart home data sets with promising results obtained. In the thesis, we also illustrate how the proposed unsupervised methodology could be used to support exploratory behavior analysis for elderly care.

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The rapid development of sensor technology in smartphone and wearable devices has led research to the area of human activity recognition (HAR). As a phase in HAR, applying classification models to collected sensor data is well-researched, and many of the different models can recognize activities successfully. Furthermore, some methods give successful results only using one or two sensors. The use of HAR within pain management is also an existing research field, but applying HAR to the pain treatment strategy of acceptance and commitment therapy (ACT) is not well-documented. The relevance of HAR in this context is that ACT:s core ideas are based on the perspective that daily life activities are connected to pain. In this thesis, state-of-the-art examples for sensor-based HAR applicable to ACT are provided through a literature review. Based on these findings, the practical use is assessed in order to provide a perspective to the current state of research.

In this report we investigate how well a smartphone can be used to detect the standing-to-kneeling and kneeling-to-standing postural transitions. Possible applications include measuring time spent kneeling in certain groups of workers prone to knee-straining work. Accelerometer and gyroscope data was recorded from a group of 10 volunteers while performing a set of postural transitions according to an experimental script. The set of postural transitions included the standing-to-kneeling and kneeling-to-standing transitions, in addition to a selection of transitions common in knee-straining occupations. Using recorded video, the recorded data was labeled and segmented into a data set consisting of 3-second sensor data segments in 9 different classes. The classification performance of a number of different LSTM-networks were evaluated on the data set. When evaluated in a user-specific setting, the best network achieved an overall classification accuracy of 89.4 %. The network achieved precision 0.982 and recall 0.917 for the standing-to-kneeling transitions, and precision 0.900 and recall 0.900 for the kneeling-to-standing transitions. When the same network was evaluated in a user-independent setting it achieved an overall accuracy of 66.3 %, with precision 0.720 and recall 0.746 for the standing-to-kneeling transitions, and precision 0.707 and recall 0.604 for the kneeling-to-standing transitions. The network was also evaluated in a setting where only accelerometer data was used. The achieved performance was similar to that achieved when using data from both the accelerometer and gyroscope. The classification speed of the network was evaluated on a smartphone. On a Samsung Galaxy S7 the average time needed to perform one classification was 38.5 milliseconds. The classification can therefore be done in real time. / I denna rapport undersöks möjligheten att använda en smartphone för att upptäcka posturala övergångar mellan stående och knästående ställning. Ett möjligt användningsområde för sådan detektion är att mäta mängd tid spenderad knäståendes hos vissa yrkesgrupper. Accelerometerdata och gyroskopdata spelades in från en grupp av 10 försökspersoner medan de utförde vissa posturala övergångar, vilka inkluderade övergångar från stående till knästående ställning samt från knästående till stående ställning. Genom att granska inspelad video från försöken markerades bitar av den inspelade datan som tillhörandes en viss postural övergång. Datan segmenterades och gav upphov till ett dataset bestående av 3 sekunder långa segment av sensordata i 9 olika klasser. Prestandan för ett antal olika LSTM-nätverk utvärderades på datasetet. Det bästa nätverket uppnådde en övergripande noggrannhet av 89.4 % när det utvärderades användarspecifikt. Nätverket uppnådde en precision av 0.982 och en återkallelse av 0.917 för övergångar från stående till knästående ställning, samt en precision av 0.900 och en återkallelse av 0.900 för övergångar från knästående till stående ställning. När samma nätverk utvärderades användaroberoende uppnådde det en övergripande noggrannhet av 66.3 %, med en precision av 0.720 och återkallelse av 0.746 för övergångar från stående till knästående ställning, samt en precision av 0.707 och återkallelse av 0.604 för övergångar mellan knästående och stående ställning. Nätverket utvärderades också i en konfiguration där enbart accelerometerdata nyttjades, och uppnådde liknande prestanda som när både accelerometerdata och gyroskopdata användes. Nätverkets klassificeringshastighet utvärderades på en smartphone. När klassificeringen utfördes på en Samsung Galaxy S7 var den genomsnittliga körningstiden 38.5 millisekunder, vilket är snabbt nog för att utföras i realtid.

Human activity regions (HARs) are human-centric semantic partitions where observing and/or interacting with humans is likely in indoor environments. HARs are useful for achieving successful human-robot interaction, such as in safe navigation around a building or to know where to be able to assist humans in their activities. In this thesis, a system is designed for generating HARs automatically based on data recorded by robots. This approach to generating HARs is to cluster the areas that are commonly associated with frequent human presence. In order to detect human positions, we employ state-of-the-art perception techniques. The environment that the robot patrols is assumed to be an indoor environment such as an office. We show how we can generate HARs in correct regions by clustering human position data. The experimental evaluations show that we can do so in different indoor environments, with data acquired from different sensors and that the system can handle noise. / Mänskliga aktivitetsregioner, HARs (Human Activity Regions) är människocentreraderegioner som ger en semantisk partitionering av inomhusmiljöer. HARs är användbara för att uppnå väl fungerande människarobot- interaktioner. I denna avhandling utformas ett system för att generera HARs automatiskt baserat på data från robotar. Detta görs genom att klustra observationer av människor för att på så vis få fram de områden som är associerade med frekvent mänsklig närvaro. Experiment visar att systemet kan hantera data som registrerats av olika sensorer i olika inomhusmiljöer och att det är robust. Framförallt genererar systemet en pålitlig partitionering av miljön.

Trust is explored in this thesis through the analysis of the robustness of wearable device based artificial intelligence based models to changes in data acquisition. Specifically changes in wearable device hardware and different recording sessions are explored. Three human activity recognition models are used as a vehicle to explore this: Model A which is trained using accelerometer signals recorded by a wearable sensor referred to as Astroskin, Model H which is trained using accelerometer signals from a wearable sensor referred to as the BioHarness and Model A Type 1 which was trained on Astroskin accelerometer signals that was recorded on the first session of the experimental protocol. On a test set recorded by Astroskin Model A had a 99.07% accuracy. However on a test set recorded by the BioHarness Model A had a 65.74% accuracy. On a test set recorded by BioHarness Model H had a 95.37% accuracy. However on a test set recorded by Astroskin Model H had a 29.63% accuracy. Model A Type 1 an average accuracy of 99.57% on data recorded by the same wearable sensor and same session. An average accuracy of 50.95% was obtained on a test set that was recorded by the same wearable sensor but by a different session. An average accuracy of 41.31% was obtained on data that was recorded by a different wearable sensor and same session. An average accuracy of 19.28% was obtained on data that was recorded by a different wearable sensor and different session. An out of domain discriminator for Model A Type 1 was also implemented. The out of domain discriminator was able to differentiate between the data that trained Model A Type 1 and other types (data recorded by a different wearable devices/different sessions) with an accuracy of 97.60%. / Thesis / Master of Applied Science (MASc) / The trustworthiness of artificial intelligence must be explored before society can fully reap its benefits. The element of trust that is explored in this thesis is the robustness of wearable device based artificial intelligence models to changes in data acquisition. The specific changes that are explored are changes in the wearable device used to record the input data as well as input data from different recording sessions. Using human activity recognition models as a vehicle, the results show that performance degradation occurs when the wearable device is changed and when data comes from a different recording session. An out of domain discriminator is developed to alert users when a potential performance degradation can occur.