Teenage Visitor Experience: Classification of Behavioral Dynamics in Museums

Teenagers' engagement in museums is much talked about but little research has been done to understand their behavior and inform design. Findings from co-design sessions with teenagers suggested they value games and stories when thinking about enjoyable museum tours. Informed by these findings and working with a natural history museum, we designed: a story-based tour (Turning Point) and a game-based tour (Haunted Encounters), informed by similar content. The two strategies were evaluated with 78 teenagers (15-19 years old) visiting the museum as part of an educational school trip. We assessed teenagers' personality in class; qualitative and quantitative data on their engagement, experience, and usability of the apps were collected at the museum. The triangulation of quantitative and qualitative data show personality traits mapping into different behaviors. We offer implications for the design of museum apps targeted to teenagers, a group known as difficult to reach

Rehab-Net: Deep Learning framework for Arm Movement Classification using Wearable Sensors for Stroke Rehabilitation

In this paper, we present a deep learning framework 'Rehab-Net' for effectively classifying three upper limb movements of the human arm, involving extension, flexion and rotation of the forearm which over the time could provide a measure of rehabilitation progress. The proposed framework, Rehab-Net is formulated with a personalized, light weight and low complex, customized CNN model, using 2-layers of Convolutional neural network (CNN), interleaved with pooling layers, followed by a fully-connected layer that classifies the three movements from tri-axial acceleration input data collected from the wrist. The proposed Rehab-net framework was validated on sensor data collected in two situations-a) seminaturalistic environment involving an archetypal activity of 'making-tea' with 4 stroke survivors and b) natural environment, where 10 stroke survivors were free to perform any desired arm movement for a duration of 120 minutes. We achieve an overall accuracy of 97.89% on semi-naturalistic data and 88.87% on naturalistic data which exceeded state-of-the-art learning algorithms namely, Linear Discriminant Analysis, Support Vector Machines, and k-means clustering with an average accuracy of 48.89%, 44.14% and 27.64%. Subsequently, a computational complexity analysis of the proposed model has been discussed with an eye towards hardware implementation. The clinical significance of this study is to accurately monitor the clinical progress of the rehabilitated subjects under the ambulatory settings

CNN based approach for activity recognition using a wrist-worn accelerometer

In recent years, significant advancements have taken place in human activity recognition using various machine learning approaches. However, feature engineering have dominated conventional methods involving the difficult process of optimal feature selection. This problem has been mitigated by using a novel methodology based on deep learning framework which automatically extracts the useful features and reduces the computational cost. As a proof of concept, we have attempted to design a generalized model for recognition of three fundamental movements of the human forearm performed in daily life where data is collected from four different subjects using a single wrist worn accelerometer sensor. The validation of the proposed model is done with different pre-processing and noisy data condition which is evaluated using three possible methods. The results show that our proposed methodology achieves an average recognition rate of 99.8% as opposed to conventional methods based on K-means clustering, linear discriminant analysis and support vector machine