Snippet based trajectory statistics histograms for assistive technologies

Due to increasing hospital costs and traveling time, more and more patients decide to use medical devices at home without traveling to the hospital. However, these devices are not always very straight-forward for usage, and the recent reports show that there are many injuries and even deaths caused by the wrong use of these devices. Since human supervision during every usage is impractical, there is a need for computer vision systems that would recognize actions and detect if the patient has done something wrong. In this paper, we propose to use Snippet Based Trajectory Statistics Histograms descriptor to recognize actions in two medical device usage problems; inhaler device usage and infusion pump usage. Snippet Based Trajectory Statistics Histograms encodes the motion and position statistics of densely extracted trajectories from a video. Our experiments show that by using Snippet Based Trajectory Statistics Histograms technique, we improve the overall performance for both tasks. Additionally, this method does not require heavy computation, and is suitable for real-time systems. © Springer International Publishing Switzerland 2015

Activity analysis for assistive systems

Ankara : The Department of Computer Engineering and the Graduate School of Engineering and Science of Bilkent University, 2014.Thesis (Master's) -- Bilkent University, 2014.Includes bibliographical references leaves 47-51.Although understanding and analyzing human actions is a popular research topic in computer vision, most of the research has focused on recognizing ”ordinary” actions, such as walking and jumping. Extending these methods for more specific domains, such as assistive technologies, is not a trivial task. In most cases, these applications contain more fine-grained activities with low inter-class variance and high intra-class variance. In this thesis, we propose to use motion information from snippets, or small video intervals, in order to recognize actions from daily activities. Proposed method encodes the motion by considering the motion statistics, such as the variance and the length of trajectories. It also encodes the position information by using a spatial grid. We show that such approach is especially helpful for the domain of medical device usage, which contains actions with fast movements Another contribution that we propose is to model the sequential information of actions by the order in which they occur. This is especially useful for fine-grained activities, such as cooking activities, where the visual information may not be enough to distinguish between different actions. As for the visual perspective of the problem, we propose to combine multiple visual descriptors by weighing their confidence values. Our experiments show that, temporal sequence model and the fusion of multiple descriptors significantly improve the performance when used together.İşcen, AhmetM.S

Temporal decision making using unsupervised learning

With the explosion of ubiquitous continuous sensing, on-line streaming clustering continues to attract attention. The requirements are that the streaming clustering algorithm recognize and adapt clusters as the data evolves, that anomalies are detected, and that new clusters are automatically formed as incoming data dictate. In this dissertation, we develop a streaming clustering algorithm, MU Streaming Clustering (MUSC), that is based on coupling a Gaussian mixture model (GMM) with possibilistic clustering to build an adaptive system for analyzing streaming multi-dimensional activity feature vectors. For this reason, the possibilistic C-Means (PCM) and Automatic Merging Possibilistic Clustering Method (AMPCM) are combined together to cluster the initial data points, detect anomalies and initialize the GMM. MUSC achieves our goals when tested on synthetic and real-life datasets. We also compare MUSC's performance with Sequential k-means (sk-means), Basic Sequential Clustering Algorithm (BSAS), and Modified BSAS (MBSAS) here MUSC shows superiority in the performance and accuracy. The performance of a streaming clustering algorithm needs to be monitored over time to understand the behavior of the streaming data in terms of new emerging clusters and number of outlier data points. Incremental internal Validity Indices (iCVIs) are used to monitor the performance of an on-line clustering algorithm. We study the internal incremental Davies-Bouldin (DB), Xie-Beni (XB), and Dunn internal cluster validity indices in the context of streaming data analysis. We extend the original incremental DB (iDB) to a more general version parameterized by the exponent of membership weights. Then we illustrate how the iDB can be used to analyze and understand the performance of MUSC algorithm. We give examples that illustrate the appearance of a new cluster, the effect of different cluster sizes, handling of outlier data samples, and the effect of the input order on the resultant cluster history. In addition, we investigate the internal incremental Davies-Bouldin (iDB) cluster validity index in the context of big streaming data analysis. We analyze the effect of large numbers of samples on the values of the iCVI (iDB). We also develop online versions of two modified generalized Dunn's indices that can be used for dynamic evaluation of evolving (cluster) structure in streaming data. We argue that this method is a good way to monitor the ongoing performance of online clustering algorithms and we illustrate several types of inferences that can be drawn from such indices. We compare the two new indices to the incremental Xie-Beni and Davies-Bouldin indices, which to our knowledge offer the only comparable approach, with numerical examples on a variety of synthetic and real data sets. We also study the performance of MUSC and iCVIs with big streaming data applications. We show the advantage of iCVIs in monitoring large streaming datasets and in providing useful information about the data stream in terms of emergence of a new structure, amount of outlier data, size of the clusters, and order of data samples in each cluster. We also propose a way to project streaming data into a lower space for cases where the distance measure does not perform as expected in the high dimensional space. Another example of streaming is the data acivity data coming from TigerPlace and other elderly residents' apartments in and around Columbia. MO. TigerPlace is an eldercare facility that promotes aging-in-place in Columbia Missouri. Eldercare monitoring using non-wearable sensors is a candidate solution for improving care and reducing costs. Abnormal sensor patterns produced by certain resident behaviors could be linked to early signs of illness. We propose an unsupervised method for detecting abnormal behavior patterns based on a new context preserving representation of daily activities. A preliminary analysis of the method was conducted on data collected in TigerPlace. Sensor firings of each day are converted into sequences of daily activities. Then, building a histogram from the daily sequences of a resident, we generate a single data vector representing that day. Using the proposed method, a day with hundreds of sequences is converted into a single data point representing that day and preserving the context of the daily routine at the same time. We obtained an average Area Under the Curve (AUC) of 0.9 in detecting days where elder adults need to be assessed. Our approach outperforms other approaches on the same datset. Using the context preserving representation, we develoed a multi-dimensional alert system to improve the existing single-dimensional alert system in TigerPlace. Also, this represenation is used to develop a framework that utilizes sensor sequence similarity and medical concepts extracted from the EHR to automatically inform the nursing staff when health problems are detected. Our context preserving representation of daily activities is used to measure the similarity between the sensor sequences of different days. The medical concepts are extracted from the nursing notes using MetamapLite, an NLP tool included in the Unified Medical Language System (UMLS). The proposed idea is validated on two pilot datasets from twelve Tiger Place residents, with a total of 5810 sensor days out of which 1966 had nursing notes

Description and application of the correlation between gaze and hand for the different hand events occurring during interaction with tablets

People’s activities naturally involve the coordination of gaze and hand. Research in Human-Computer Interaction (HCI) endeavours to enable users to exploit this multimodality for enhanced interaction. With the abundance of touch screen devices, direct manipulation of an interface has become a dominating interaction technique. Although touch enabled devices are prolific in both public and private spaces, interactions with these devices do not fully utilise the benefits from the correlation between gaze and hand. Touch enabled devices do not employ the richness of the continuous manual activity above their display surface for interaction and a lot of information expressed by users through their hand movements is ignored. This thesis aims at investigating the correlation between gaze and hand during natural interaction with touch enabled devices to address these issues. To do so, we set three objectives. Firstly, we seek to describe the correlation between gaze and hand in order to understand how they operate together: what is the spatial and temporal relationship between these modalities when users interact with touch enabled devices? Secondly, we want to know the role of some of the inherent factors brought by the interaction with touch enabled devices on the correlation between gaze and hand, because identifying what modulates the correlation is crucial to design more efficient applications: what are the impacts of the individual differences, the task characteristics and the features of the on-screen targets? Thirdly, as we want to see whether additional information related to the user can be extracted from the correlation between gaze and hand, we investigate the latter for the detection of users’ cognitive state while they interact with touch enabled devices: can the correlation reveal the users’ hesitation? To meet the objectives, we devised two data collections for gaze and hand. In the first data collection, we cover the manual interaction on-screen. In the second data collection, we focus instead on the manual interaction in-the-air. We dissect the correlation between gaze and hand using three common hand events users perform while interacting with touch enabled devices. These events comprise taps, stationary hand events and the motion between taps and stationary hand events. We use a tablet as a touch enabled device because of its medium size and the ease to integrate both eye and hand tracking sensors. We study the correlation between gaze and hand for tap events by collecting gaze estimation data and taps on tablet in the context of Internet related tasks, representative of typical activities executed using tablets. The correlation is described in the spatial and temporal dimensions. Individual differences and effects of the task nature and target type are also investigated. To study the correlation between gaze and hand when the hand is in a stationary situation, we conducted a data collection in the context of a Memory Game, chosen to generate enough cognitive load during playing while requiring the hand to leave the tablet’s surface. We introduce and evaluate three detection algorithms, inspired by eye tracking, based on the analogy between gaze and hand patterns. Afterwards, spatial comparisons between gaze and hands are analysed to describe the correlation. We study the effects on the task difficulty and how the hesitation of the participants influences the correlation. Since there is no certain way of knowing when a participant hesitates, we approximate the hesitation with the failure of matching a pair of already seen tiles. We study the correlation between gaze and hand during hand motion between taps and stationary hand events from the same data collection context than the case mentioned above. We first align gaze and hand data in time and report the correlation coefficients in both X and Y axis. After considering the general case, we examine the impact of the different factors implicated in the context: participants, task difficulty, duration and type of the hand motion. Our results show that the correlation between gaze and hand, throughout the interaction, is stronger in the horizontal dimension of the tablet rather than in its vertical dimension, and that it varies widely across users, especially spatially. We also confirm the eyes lead the hand for target acquisition. Moreover, we find out that the correlation between gaze and hand when the hand is in the air above the tablet’s surface depends on where the users look at on the tablet. As well, we show that the correlation during eye and hand during stationary hand events can indicate the users’ indecision, and that while the hand is moving, the correlation depends on different factors, such as the degree of difficulty of the task performed on the tablet and the nature of the event before/after the motion

Augmented Reality in Sport Broadcasting

For a large portion of its history, sport broadcasting has been stagnant when it comes to incorporating new and innovative technologies. However, due to declining viewership and consumer desire for customizable content, augmented reality graphics have begun to be incorporated into multiple sport broadcast products. In fact, the UEFA Champions League, NBA, NFL, and NHL have all used or indicated their intention to utilize AR graphics in future broadcasts. Considering that media rights revenue is the main source of revenue to sport properties and organizations, it is important to carefully consider how the core product (the broadcast) is presented. The study examined consumer attitudes and intentions towards AR in sport broadcasts by utilizing three types of broadcasts of an NBA game. One of the broadcasts was a traditional broadcast format with no AR enhancement and the other two were enhanced with AR graphics, a coach-mode broadcast that featured AR player tracking and play diagramming while the other enhanced broadcast, mascot-mode, featured AR graphics similar to a video game with over-the-top animations. Results of the current study provide insight into consumer preferences towards AR in sport broadcasting and guidance to sport properties planning to utilize broadcast AR graphics. Specifically, that sport consumers were significantly more likely to re-view (p \u3c .05) and recommend via word of mouth (p \u3c .05) the coach-mode AR than the mascot-mode AR. Sport involvement was a significant factor for how sport fans perceive the AR broadcast types through incorporating the perspective of the elaboration likelihood model

Kinematic assessment for stroke patients in a stroke game and a daily activity recognition and assessment system

Stroke is the leading cause of serious, long-term disabilities among which deficits in motor abilities in arms or legs are most common. Those who suffer a stroke can recover through effective rehabilitation which is delicately personalized. To achieve the best personalization, it is essential for clinicians to monitor patients' health status and recovery progress accurately and consistently. Traditionally, rehabilitation involves patients performing exercises in clinics where clinicians oversee the procedure and evaluate patients' recovery progress. Following the in-clinic visits, additional home practices are tailored and assigned to patients. The in-clinic visits are important to evaluate recovery progress. The information collected can then help clinicians customize home practices for stroke patients. However, as the number of in-clinic sessions is limited by insurance policies, the recovery information collected in-clinic is often insufficient. Meanwhile, the home practice programs report low adherence rates based on historic data. Given that clinicians rely on patients to self-report adherence, the actual adherence rate could be even lower. Despite the limited feedback clinicians could receive, the measurement method is subjective as well. In practice, classic clinical scales are mostly used for assessing the qualities of movements and the recovery status of patients. However, these clinical scales are evaluated subjectively with only moderate inter-rater and intra-rater reliabilities. Taken together, clinicians lack a method to get sufficient and accurate feedback from patients, which limits the extent to which clinicians can personalize treatment plans. This work aims to solve this problem. To help clinicians obtain abundant health information regarding patients' recovery in an objective approach, I've developed a novel kinematic assessment toolchain that consists of two parts. The first part is a tool to evaluate stroke patients' motions collected in a rehabilitation game setting. This kinematic assessment tool utilizes body-tracking in a rehabilitation game. Specifically, a set of upper body assessment measures were proposed and calculated for assessing the movements using skeletal joint data. Statistical analysis was applied to evaluate the quality of upper body motions using the assessment outcomes. Second, to classify and quantify home activities for stroke patients objectively and accurately, I've developed DARAS, a daily activity recognition and assessment system that evaluates daily motions in a home setting. DARAS consists of three main components: daily action logger, action recognition part, and assessment part. The logger is implemented with a Foresite system to record daily activities using depth and skeletal joint data. Daily activity data in a realistic environment were collected from sixteen post-stroke participants. The collection period for each participant lasts three months. An ensemble network for activity recognition and temporal localization was developed to detect and segment the clinically relevant actions from the recorded data. The ensemble network fuses the prediction outputs from customized 3D Convolutional-De-Convolutional, customized Region Convolutional 3D network and a proposed Region Hierarchical Co-occurrence network which learns rich spatial-temporal features from either depth data or joint data. The per-frame precision and the per-action precision were 0.819 and 0.838, respectively, on the validation set. For the recognized actions, the kinematic assessments were performed using the skeletal joint data, as well as the longitudinal assessments. The results showed that, compared with non-stroke participants, stroke participants had slower hand movements, were less active, and tended to perform fewer hand manipulation actions. The assessment outcomes from the proposed toolchain help clinicians to provide more personalized rehabilitation plans that benefit patients.Includes bibliographical references

Engineering systematic musicology : methods and services for computational and empirical music research

One of the main research questions of *systematic musicology* is concerned with how people make sense of their musical environment. It is concerned with signification and meaning-formation and relates musical structures to effects of music. These fundamental aspects can be approached from many different directions. One could take a cultural perspective where music is considered a phenomenon of human expression, firmly embedded in tradition. Another approach would be a cognitive perspective, where music is considered as an acoustical signal of which perception involves categorizations linked to representations and learning. A performance perspective where music is the outcome of human interaction is also an equally valid view. To understand a phenomenon combining multiple perspectives often makes sense. The methods employed within each of these approaches turn questions into concrete musicological research projects. It is safe to say that today many of these methods draw upon digital data and tools. Some of those general methods are feature extraction from audio and movement signals, machine learning, classification and statistics. However, the problem is that, very often, the *empirical and computational methods require technical solutions* beyond the skills of researchers that typically have a humanities background. At that point, these researchers need access to specialized technical knowledge to advance their research. My PhD-work should be seen within the context of that tradition. In many respects I adopt a problem-solving attitude to problems that are posed by research in systematic musicology. This work *explores solutions that are relevant for systematic musicology*. It does this by engineering solutions for measurement problems in empirical research and developing research software which facilitates computational research. These solutions are placed in an engineering-humanities plane. The first axis of the plane contrasts *services* with *methods*. Methods *in* systematic musicology propose ways to generate new insights in music related phenomena or contribute to how research can be done. Services *for* systematic musicology, on the other hand, support or automate research tasks which allow to change the scope of research. A shift in scope allows researchers to cope with larger data sets which offers a broader view on the phenomenon. The second axis indicates how important Music Information Retrieval (MIR) techniques are in a solution. MIR-techniques are contrasted with various techniques to support empirical research. My research resulted in a total of thirteen solutions which are placed in this plane. The description of seven of these are bundled in this dissertation. Three fall into the methods category and four in the services category. For example Tarsos presents a method to compare performance practice with theoretical scales on a large scale. SyncSink is an example of a service

Diseño y control de exoesqueleto robótico para la rehabilitación y asistencia de los movimientos de la mano

Programa de Doctorado en Tecnologías Industriales y de Telecomunicación por la Universidad Miguel Hernández de ElcheHands are one of the main instruments used by humans for interacting with physical environment. Furthermore, hands play an important role in other aspects of daily living such as non-verbal communication or postural control assisted by external supports. Therefore, individuals that suffer some kind of hand impairment become dependent in many common situations, reducing their quality of life. Developments in the field of robotics result in potential solutions to overcome their dependency. In particular, wearable devices such as exoskeletons can help to lessen the impact of the impairment by becoming a new tool for providing more intense and effective rehabilitation therapies, or by their potential applications to assist people during their activities of daily living in a domestic environment. This Doctoral Thesis focuses on the development of a robotic exoskeleton that, due to its constructive features, can be applied to both rehabilitation and assitance environments. As an innovation, this exoskeleton has a new type of force sensor architecture, integrable in the device, favoring the lightness and portability of the equipment and offering a versatile force control interface in a multitude of environments. Along with the force interface, other types of interfaces based on biological and kinematic parameters are studied, in order to provide the system with the necessary versatility to adapt to different user profiles. In addition, two practical applications of the device are presented in complex rehabilitation settings and everyday situations not previously studied. The results of this work are compiled in four publications in journals indexed in the Journal Citation Reports (JCR). The publication Multimodal robotic system for upper-limb rehabilitation in physical environment studies the integration of the hand exoskeleton in a system of robots and sensors that allow the implementation of manipulative therapies in real environments, using a human-machine interface based on electromyographic signals. As an alternative to electromyography for advanced stages of rehabilitation, new interfaces based on motion capture and force feedback are proposed, results are published in the paper Hand exoskeleton for rehabilitation therapies with integrated optical force sensor. A detailed description of the force sensor integrated in the exoskeleton can be found in the publication Customizable optical force sensor for fast prototyping and cost-effective applications. Finally, the publication Exploring new potential applications for Hand Exoskeletons: Power grip to assist human standing studies the applicability of hand exoskeletons to improve postural control