text: Bimanual gestures are of the utmost importance for the study of motor coordination in humans and in everyday activities. A reliable detection of bimanual gestures in unconstrained environments is fundamental for their clinical study and to assess common activities of daily living. This paper investigates techniques for a reliable, unconstrained detection and classification of bimanual gestures. It assumes the availability of inertial data originating from the two hands arms, builds upon a previously developed technique for gesture modelling based on Gaussian Mixture Modelling (GMM) and Gaussian Mixture Regression (GMR), and compares different modelling and classification techniques, which are based on a number of assumptions inspired by literature about how bimanual gestures are represented and modelled in the brain. Experiments show results related to 5 everyday bimanual activities, which have been selected on the basis of three main parameters: (not) constraining the two hands by a physical tool, (not) requiring a specific sequence of single-hand gestures, being recursive (or not). In the best performing combination of modeling approach and classification technique, five out of five activities are recognized up to an accuracy of 97 , a precision of 82 and a level of recall of 100 .
  @cite_9: Accurate recognition and tracking of human activities is an important goal of ubiquitous computing. Recent advances in the development of multi-modal wearable sensors enable us to gather rich datasets of human activities. However, the problem of automatically identifying the most useful features for modeling such activities remains largely unsolved. In this paper we present a hybrid approach to recognizing activities, which combines boosting to discriminatively select useful features and learn an ensemble of static classifiers to recognize different activities, with hidden Markov models (HMMs) to capture the temporal regularities and smoothness of activities. We tested the activity recognition system using over 12 hours of wearable-sensor data collected by volunteers in natural unconstrained environments. The models succeeded in identifying a small set of maximally informative features, and were able identify ten different human activities with an accuracy of 95 .
  @cite_7: A generic framework for the automated classification of human movements using an accelerometry monitoring system is introduced. The framework was structured around a binary decision tree in which movements were divided into classes and subclasses at different hierarchical levels. General distinctions between movements were applied in the top levels, and successively more detailed subclassifications were made in the lower levels of the tree. The structure was modular and flexible: parts of the tree could be reordered, pruned or extended, without the remainder of the tree being affected. This framework was used to develop a classifier to identify basic movements from the signals obtained from a single, waist-mounted triaxial accelerometer. The movements were first divided into activity and rest. The activities were classified as falls, walking, transition between postural orientations, or other movement. The postural orientations during rest were classified as sitting, standing or lying. In controlled laboratory studies in which 26 normal, healthy subjects carried out a set of basic movements, the sensitivity of every classification exceeded 87 , and the specificity exceeded 94 ; the overall accuracy of the system, measured as the number of correct classifications across all levels of the hierarchy, was a sensitivity of 97.7 and a specificity of 98.7 over a data set of 1309 movements.
  @cite_2: In this position paper we will focus on wearable activity recognitions tools in regard to their function of detecting human activities and thus enabling the user to recall everyday experience in a new way. The capabilities of activity recognition to detect, store and present activities to the person who has performed it can not only help to recall the activities but also encourage the user to remember experiences related to the activities. In order to demonstrate this, we present two projects (cases) in which wearable activity recognition is used to support the users' recall capabilities. In the next step, we present a narrative theory of action and mind, which focuses on how humans retrospectively interpret and structure personal experience in their minds, their so called autobiographical memory. Finally, we present some further concepts and distinctions about what it means to memorize and recall personal data.
summary: Wearable systems for the automatic recognition of human gestures and full-body movements typically rely on inertial information. Accelerometers prove to be the most informative sensor for the task @cite_9 . To comply with end users' constraints related to the impact of the monitoring system on their appearance and freedom of motion, most solutions adopt a single sensing device, either located at the waist @cite_7 or, as it is becoming more and more common, at the wrist @cite_2 .