Dementia, music and biometric gaming: Rising to the Dementia Challenge

In 2012, the U.K. government launched its Dementia Challenge, authorizing additional funding for dementia research and health care. The search for curative medicines is ongoing, but scientific research reveals evidence that music can play a positive role in general health, and in dementia and Alzheimer’s disease in particular. This article considers whether some of the challenges that dementia presents could be addressed through music therapy and proposes that biometric gaming might offer one means of channeling such associated health benefits to sufferers of dementia, even in the final stages of the disease

Plug-in to fear: game biosensors and negative physiological responses to music

The games industry is beginning to embark on an ambitious journey into the world of biometric gaming in search of more exciting and immersive gaming experiences. Whether or not biometric game technologies hold the key to unlock the “ultimate gaming experience” hinges not only on technological advancements alone but also on the game industry’s understanding of physiological responses to stimuli of different kinds, and its ability to interpret physiological data in terms of indicative meaning. With reference to horror genre games and music in particular, this article reviews some of the scientific literature relating to specific physiological responses induced by “fearful” or “unpleasant” musical stimuli, and considers some of the challenges facing the games industry in its quest for the ultimate “plugged-in” experience

The CDVPlex biometric cinema: sensing physiological responses to emotional stimuli in film

We describe a study conducted to investigate the potential correlations between human subject responses to emotional stimuli in movies, and observed biometric responses. The experimental set-up and procedure are described, including details of the range of sensors used to detect and record observed physiological data (such as heart-rate, galvanic skin response, body temperature and movement). Finally, applications and future analysis of the results of the study are discussed

Biometric Identification using Phonocardiogram

Phonocardiogram (PCG) signals as a biometric is a new and novel method for user identification. Use of PCG signals for user recognition is a highly reliable method because heart sounds are produced by internal organs and cannot be forged easily as compared to other recognition systems such as fingerprint, iris, DNA etc. PCG signals have been recorded using an electronic stethoscope. Database of heart sound is made using the electronic stethoscope. In the beginning, heart sounds for different classes is observed in time as well as frequency for their uniqueness for each class. The first step performed is to extract features from the recorded heart signals. We have implemented LFBC algorithm as a feature extraction algorithm to get the cepstral component of heart sound. The next objective is to classify these feature vectors to recognize a person. A classification algorithm is first trained using a training sequence for each user to generate unique features for each user. During the testing period, the classifier uses the stored training attributes for each user and uses them to match or identify the testing sequence. We have used LBG-VQ and GMM for the classification of user classes. Both the algorithms are iterative, robust and well established methods for user identification. We have implemented the normalization at two places; first, before feature extraction; then just after the feature extraction in case of GMM classifier which is not proposed in earlier literature

Gait Verification using Knee Acceleration Signals

A novel gait recognition method for biometric applications is proposed. The approach has the following distinct features. First, gait patterns are determined via knee acceleration signals, circumventing difficulties associated with conventional vision-based gait recognition methods. Second, an automatic procedure to extract gait features from acceleration signals is developed that employs a multiple-template classification method. Consequently, the proposed approach can adjust the sensitivity and specificity of the gait recognition system with great flexibility. Experimental results from 35 subjects demonstrate the potential of the approach for successful recognition. By setting sensitivity to be 0.95 and 0.90, the resulting specificity ranges from 1 to 0.783 and 1.00 to 0.945, respectively

Biometric responses to music-rich segments in films: the CDVPlex

Summarising or generating trailers for films or movies involves finding the highlights within those films, those segments where we become most afraid, happy, sad, annoyed, excited, etc. In this paper we explore three questions related to automatic detection of film highlights by measuring the physiological responses of viewers of those films. Firstly, whether emotional highlights can be detected through viewer biometrics, secondly whether individuals watching a film in a group experience similar emotional reactions as others in the group and thirdly whether the presence of music in a film correlates with the occurrence of emotional highlights. We analyse the results of an experiment known as the CDVPlex, where we monitored and recorded physiological reactions from people as they viewed films in a controlled cinema-like environment. A selection of films were manually annotated for the locations of their emotive contents. We then studied the physiological peaks identified among participants while viewing the same film and how these correlated with emotion tags and with music. We conclude that these are highly correlated and that music-rich segments of a film do act as a catalyst in stimulating viewer response, though we don't know what exact emotions the viewers were experiencing. The results of this work could impact the way in which we index movie content on PVRs for example, paying special significance to movie segments which are most likely to be highlights

Semi-automatic semantic enrichment of raw sensor data

One of the more recent sources of large volumes of generated data is sensor devices, where dedicated sensing equipment is used to monitor events and happenings in a wide range of domains, including monitoring human biometrics. In recent trials to examine the effects that key moments in movies have on the human body, we fitted fitted with a number of biometric sensor devices and monitored them as they watched a range of dierent movies in groups. The purpose of these experiments was to examine the correlation between humans' highlights in movies as observed from biometric sensors, and highlights in the same movies as identified by our automatic movie analysis techniques. However,the problem with this type of experiment is that both the analysis of the video stream and the sensor data readings are not directly usable in their raw form because of the sheer volume of low-level data values generated both from the sensors and from the movie analysis. This work describes the semi-automated enrichment of both video analysis and sensor data and the mechanism used to query the data in both centralised environments, and in a peer-to-peer architecture when the number of sensor devices grows to large numbers. We present and validate a scalable means of semi-automating the semantic enrichment of sensor data, thereby providing a means of large-scale sensor management