Smart Electrically Assisted Bicycles as Health Monitoring Systems: A Review

This paper aims to provide a review of the electrically assisted bicycles (also known as e-bikes) used for recovery of the rider’s physical and physiological information, monitoring of their health state, and adjusting the “medical” assistance accordingly. E-bikes have proven to be an excellent way to do physical activity while commuting, thus improving the user’s health and reducing air pollutant emissions. Such devices can also be seen as the first step to help unhealthy sedentary people to start exercising with reduced strain. Based on this analysis, the need to have e-bikes with artificial intelligence (AI) systems that recover and processe a large amount of data is discussed in depth. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines were used to complete the relevant papers’ search and selection in this systematic review.

Are Those Steps Worth Your Privacy?

Fitness trackers are increasingly popular. The data they collect provides substantial benefits to their users, but it also creates privacy risks. In this work, we investigate how fitness-tracker users perceive the utility of the features they provide and the associated privacy-inference risks. We conduct a longitudinal study composed of a four-month period of fitness-tracker use (N = 227), followed by an online survey (N = 227) and interviews (N = 19). We assess the users' knowledge of concrete privacy threats that fitness-tracker users are exposed to (as demonstrated by previous work), possible privacy-preserving actions users can take, and perceptions of utility of the features provided by the fitness trackers. We study the potential for data minimization and the users' mental models of how the fitness tracking ecosystem works. Our findings show that the participants are aware that some types of information might be inferred from the data collected by the fitness trackers. For instance, the participants correctly guessed that sexual activity could be inferred from heart-rate data. However, the participants did not realize that also the non-physiological information could be inferred from the data. Our findings demonstrate a high potential for data minimization, either by processing data locally or by decreasing the temporal granularity of the data sent to the service provider. Furthermore, we identify the participants' lack of understanding and common misconceptions about how the Fitbit ecosystem works.

Can Formal Security Verification Really Be Optional? Scrutinizing the Security of IMD Authentication Protocols

The need for continuous monitoring of physiological information of critical organs of the human body, combined with the ever-growing field of electronics and sensor technologies and the vast opportunities brought by 5G connectivity, have made implantable medical devices (IMDs) the most necessitated devices in the health arena. IMDs are very sensitive since they are implanted in the human body, and the patients depend on them for the proper functioning of their vital organs. Simultaneously, they are intrinsically vulnerable to several attacks mainly due to their resource limitations and the wireless channel utilized for data transmission. Hence, failing to secure them would put the patient’s life in jeopardy and damage the reputations of the manufacturers. To date, various researchers have proposed different countermeasures to keep the confidentiality, integrity, and availability of IMD systems with privacy and safety specifications. Despite the appreciated efforts made by the research community, there are issues with these proposed solutions. Principally, there are at least three critical problems. (1) Inadequate essential capabilities (such as emergency authentication, key update mechanism, anonymity, and adaptability); (2) heavy computational and communication overheads; and (3) lack of rigorous formal security verification. Motivated by this, we have thoroughly analyzed the current IMD authentication protocols by utilizing two formal approaches: the Burrows–Abadi–Needham logic (BAN logic) and the Automated Validation of Internet Security Protocols and Applications (AVISPA). In addition, we compared these schemes against their security strengths, computational overheads, latency, and other vital features, such as emergency authentications, key update mechanisms, and adaptabilities.

An accelerometer-derived ballistocardiogram method for detecting heartrates in free-ranging marine mammals

Physio-logging methods, which use animal-borne devices to record physiological variables, are entering a new era driven by advances in sensor development. However, existing datasets collected with traditional bio-loggers, such as accelerometers, still contain untapped eco-physiological information. Here we present a computational method for extracting heartrate from high-resolution accelerometer data using a ballistocardiogram. We validated our method with simultaneous accelerometer-electrocardiogram tag deployments in a controlled setting on a killer whale (Orcinus orca) and demonstrate the method recovers previously observed cardiovascular patterns in a blue whale (Balaenoptera musculus), including the magnitude of apneic bradycardia and increase in heart rate prior to and during ascent. Our ballistocardiogram method may be applied to mine heart rates from previously collected accelerometery and expand our understanding of comparative cardiovascular physiology.

Exploring the associations between transcript levels and fluxes in constraint-based models of metabolism

Background Several computational methods have been developed that integrate transcriptomics data with genome-scale metabolic reconstructions to increase accuracy of inferences of intracellular metabolic flux distributions. Even though existing methods use transcript abundances as a proxy for enzyme activity, each method uses a different hypothesis and assumptions. Most methods implicitly assume a proportionality between transcript levels and flux through the corresponding function, although these proportionality constant(s) are often not explicitly mentioned nor discussed in any of the published methods. E-Flux is one such method and, in this algorithm, flux bounds are related to expression data, so that reactions associated with highly expressed genes are allowed to carry higher flux values. Results Here, we extended E-Flux and systematically evaluated the impact of an assumed proportionality constant on model predictions. We used data from published experiments with Escherichia coli and Saccharomyces cerevisiae and we compared the predictions of the algorithm to measured extracellular and intracellular fluxes. Conclusion We showed that detailed modelling using a proportionality constant can greatly impact the outcome of the analysis. This increases accuracy and allows for extraction of better physiological information.

Using in-game biofeedback to induce player serenity

<p>Video games no longer predominantly emphasize mere entertainment or excitement, they now investigate more complex emotions. As a new dimension of player input, biofeedback (University of Maryland Baltimore Washington Medical Center, 2015) can be used to track a player’s body signals in real-time. This biofeedback can impact a player’s experience during gameplay and making a game affective. This research aims to design game mechanics to connect game environments with a player’s physiological data, and to thereby trigger a serene gameplay experience. The development process was based on 1) game design strategies for inducing serenity, 2) design methods for biofeedback interaction in video games. Combined with these theoretical approaches, this research followed an iterative design process by prototyping, observations, individual interviews, questionnaires, and data analysis. The player’s physiological information was detected through three types of sensor: A heart rate (HR) sensor, a galvanic skin response (GSR) sensor, and a webcam with a software library for facial expressions. The game system adopted certain design elements such as color schemes to communicate the biological information these sensors gathered. There are two hypotheses in this research. One is that the adjustments of the game environment based on the player's physiological information can impact the relationship between the player and the game. The other one is that changing the game environment’s color schemes according to the player’s physiological data can strengthen their emotions. The final output was a brief (2-5 minutes) 3D exploration game attached with sensors to players. The game contained abstract nature-related visual elements and non-competitive mechanics that were applicable for biofeedback-based interaction. The result of final prototype showed that the nature-related elements and the adjustments of the color schemes in the game helped make players feel serene. The biofeedback-based interactions were effective because they helped some players feel more connected to the game. Ultimately, this work is expected to make the player experience more personal rather than generic and improve the game’s replayability.</p>

Using in-game biofeedback to induce player serenity

<p>Video games no longer predominantly emphasize mere entertainment or excitement, they now investigate more complex emotions. As a new dimension of player input, biofeedback (University of Maryland Baltimore Washington Medical Center, 2015) can be used to track a player’s body signals in real-time. This biofeedback can impact a player’s experience during gameplay and making a game affective. This research aims to design game mechanics to connect game environments with a player’s physiological data, and to thereby trigger a serene gameplay experience. The development process was based on 1) game design strategies for inducing serenity, 2) design methods for biofeedback interaction in video games. Combined with these theoretical approaches, this research followed an iterative design process by prototyping, observations, individual interviews, questionnaires, and data analysis. The player’s physiological information was detected through three types of sensor: A heart rate (HR) sensor, a galvanic skin response (GSR) sensor, and a webcam with a software library for facial expressions. The game system adopted certain design elements such as color schemes to communicate the biological information these sensors gathered. There are two hypotheses in this research. One is that the adjustments of the game environment based on the player's physiological information can impact the relationship between the player and the game. The other one is that changing the game environment’s color schemes according to the player’s physiological data can strengthen their emotions. The final output was a brief (2-5 minutes) 3D exploration game attached with sensors to players. The game contained abstract nature-related visual elements and non-competitive mechanics that were applicable for biofeedback-based interaction. The result of final prototype showed that the nature-related elements and the adjustments of the color schemes in the game helped make players feel serene. The biofeedback-based interactions were effective because they helped some players feel more connected to the game. Ultimately, this work is expected to make the player experience more personal rather than generic and improve the game’s replayability.</p>

Thermal face recognition under different conditions

Background A thermal face recognition under different conditions is proposed in this article. The novelty of the proposed method is applying temperature information in the recognition of thermal face. The physiological information is obtained from the face using a thermal camera, and a machine learning classifier is utilized for thermal face recognition. The steps of preprocessing, feature extraction and classification are incorporated in training phase. First of all, by using Bayesian framework, the human face can be extracted from thermal face image. Several thermal points are selected as a feature vector. These points are utilized to train Random Forest (RF). Random Forest is a supervised learning algorithm. It is an ensemble of decision trees. Namely, RF merges multiple decision trees together to obtain a more accurate classification. Feature vectors from the testing image are fed into the classifier for face recognition. Results Experiments were conducted under different conditions, including normal, adding noise, wearing glasses, face mask, and glasses with mask. To compare the performance with the convolutional neural network-based technique, experimental results of the proposed method demonstrate its robustness against different challenges. Conclusions Comparisons with other techniques demonstrate that the proposed method is robust under less feature points, which is around one twenty-eighth to one sixtieth of those by other classic methods.