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Abstract. Capabilities of domestic service robots could be further improved, if the robot is equipped with an ability to recognize activities performed by humans in its sensory range. For example in a simple scenario a floor cleaning robot can vacuum the kitchen floor after recognizing human activity "cooking in the kitchen". Most of the complex human activities can be sub divided into simple activities which can later used for recognize complex activities. Activities like "take meditation" can be sub divided into simple activities like "opening pill container" and "drinking water". However, even recognizing simple activities are highly challenging due to the similarities between some inter activities and dissimilarities of intra activities which are performed by different people, body poses and orientations. Even a simple human activity like "drinking water" can be performed while the subject is in different body poses like sitting, standing or walking. Therefore building machine learning techniques to recognize human activities with such complexities is non trivial. To address this issue, we propose a human activity recognition technique that uses 3D skeleton features produced by a depth camera. The algorithm incorporates importance weights for skeleton 3D joints according to the activity being performed. This allows the algorithm to ignore the confusing or irrelevant features while relying on informative features. Later these joints were ensembled together to train Dynamic Bayesian Networks (DBN), which is then used to infer human activities based on likelihoods. The proposed activity recognition technique is tested on a publicly available dataset and UTS experiments with overall accuracies of 85% and 90%.

Microbial corrosion of concrete is a severe problem that significantly reduces the service life of underground sewers in countries around the globe. Therefore, water utilities are actively looking for in-situ sensors that can quantify the biologically induced concrete corrosion levels, in order to carry out preventive maintenance before any catastrophic failures. As a solution, this paper introduces a drill-resistance based sensor that can accurately measure the depth of the microbiologically corroded concrete layer. A prototype sensor was developed and evaluated in laboratory test conditions. The lab experiments proved that the developed sensor has the ability to measure the depth of the microbiologically corroded concrete with millimeter level of accuracy. Additionally, the sensor can also locate and accurately measure the size of concrete aggregates as well as potential cracks, effectively creating a sub-surface 'scan' of the concrete at the targeted point of interest. Therefore, providing valuable extra information for assessing the condition of the sewer concrete.

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Abstract-Robots are often required to operate in environments where humans are not present, but yet require the human context information for better human robot interaction. Even humans are present in the environment, detecting their presence in cluttered environments could be challenging. As a solution to this problem, this paper presents the concept of affordancemap which learns human context by looking at geometric features of the environment. Instead of observing real humans to learn human context, it uses virtual human models and their relationships with the environment to map hidden human affordances in 3D scenes. The affordance-map learning problem is formulated as a multi label classification problem that can be learned using cost-sensitive SVM. Experiments carried out in a real 3D scene dataset recorded promising results and proved the applicability of affordance-map for mapping human context.