Enhancing Activity Recognition by Fusing Inertial and Biometric Information

Activity recognition is an active research field nowadays, as it enables the development of highly adaptive applications, e.g. in the field of personal health. In this paper, a light high-level fusion algorithm to detect the activity that an individual is performing is presented. The algorithm relies on data gathered from accelerometers placed on different parts of the body, and on biometric sensors. Inertial sensors allow detecting activity by analyzing signal features such as amplitude or peaks. In addition, there is a relationship between the activity intensity and biometric response, which can be considered together with acceleration data to improve the accuracy of activity detection. The proposed algorithm is designed to work with minimum computational cost, being ready to run in a mobile device as part of a context-aware application. In order to enable different user scenarios, the algorithm offers best-effort activity estimation: its quality of estimation depends on the position and number of the available inertial sensors, and also on the presence of biometric information

Desing and Validation of a Light Inference System to Support Embedded Context Reasoning

Embedded context management in resource-constrained devices (e.g. mobile phones, autonomous sensors or smart objects) imposes special requirements in terms of lightness for data modelling and reasoning. In this paper, we explore the state-of-the-art on data representation and reasoning tools for embedded mobile reasoning and propose a light inference system (LIS) aiming at simplifying embedded inference processes offering a set of functionalities to avoid redundancy in context management operations. The system is part of a service-oriented mobile software framework, conceived to facilitate the creation of context-aware applications—it decouples sensor data acquisition and context processing from the application logic. LIS, composed of several modules, encapsulates existing lightweight tools for ontology data management and rule-based reasoning, and it is ready to run on Java-enabled handheld devices. Data management and reasoning processes are designed to handle a general ontology that enables communication among framework components. Both the applications running on top of the framework and the framework components themselves can configure the rule and query sets in order to retrieve the information they need from LIS. In order to test LIS features in a real application scenario, an ‘Activity Monitor’ has been designed and implemented: a personal health-persuasive application that provides feedback on the user’s lifestyle, combining data from physical and virtual sensors. In this case of use, LIS is used to timely evaluate the user’s activity level, to decide on the convenience of triggering notifications and to determine the best interface or channel to deliver these context-aware alerts.

Outer product-based fusion of smartwatch sensor data for human activity recognition

The advent of IoT devices in combination with Human Activity Recognition (HAR) technologies can contribute to battle with sedentariness by continuously monitoring the users' daily activities. With this information, autonomous systems could detect users' physical weaknesses and plan personalized training routines to improve them. This work investigates the multimodal fusion of smartwatch sensor data for HAR. Specifically, we exploit pedometer, heart rate, and accelerometer information to train unimodal and multimodal models for the task at hand. The models are trained end-to-end, and we compare the performance of dedicated Recurrent Neural Network-based (RNN) and Convolutional Neural Network-based (CNN) architectures to extract deep learnt representations from the input modalities. To fuse the embedded representations when training the multimodal models, we investigate a concatenation-based and an outer product-based approach. This work explores the harAGE dataset, a new dataset for HAR collected using a Garmin Vivoactive 3 device with more than 17 h of data. Our best models obtain an Unweighted Average Recall (UAR) of 95.6, 69.5, and 60.8% when tackling the task as a 2-class, 7-class, and 10-class classification problem, respectively. These performances are obtained using multimodal models that fuse the embedded representations extracted with dedicated CNN-based architectures from the pedometer, heart rate, and accelerometer modalities. The concatenation-based fusion scores the highest UAR in the 2-class classification problem, while the outer product-based fusion obtains the best performances in the 7-class and the 10-class classification problems

Parenting practices related to positive eating, physical activity and sedentary behaviors in children: A qualitative exploration of strategies used by parents to navigate the obesigenic environment

Parents model and teach early health practices that persist into adulthood by establishing a foundation through which children understand related family beliefs, values, and expectations. The environment in which parents socialize children\u27s eating, physical activity, and screen-related behaviors has changed and has been widely faulted in the obesity epidemic. This phenomenological study examined the intentions, reflections, and strategies in which a purposefully selected group of mothers, scoring highly on the Family Nutrition and Physical Activity screening tool, shaped family culture related to physical activity, addressed screen-time behaviors, and established positive eating related routines. Findings related to mothers\u27 knowledge and belief systems about parenting within this domain pointed to the impact of family health history and mothers\u27 own upbringing, reinforcing the powerful nature of early habit formation. Mothers prioritized this parenting domain and were intentional in their efforts, describing the power of modeling positive obesity-related behaviors and creating a culture that promoted activity over sedentariness. By focusing on establishing positive behaviors at home, and framing choices and opportunities in support of child autonomy, mothers believed they were preparing children to resist threats from the obesigenic environment. This study presents a strengths perspective and imparts a new narrative which serves to complement existing obesity research in representative and at-risk populations. Findings may inform obesity prevention and intervention programs as well as parenting education curricula

Exploring Relationships Between Sleep, Physical Activity, Diet And Glycaemic Control During And After Gestational Diabetes: Studies With Activity Monitors

Gestational diabetes (GDM) is a common pregnancy complication. It is associated with an increased risk of type 2 diabetes mellitus (T2DM) in the mother. Emerging evidence supports sleep as an additional modifiable risk factor of not only T2DM and glycaemic control but influences diet and exercise which are the mainstays of lifestyle intervention for GDM and T2DM prevention. Technologies such as activity monitors that can track physical activity and sleep can function as research tools with their objectivity and convenient but can also be integrated into lifestyle programs through their accessibility and interactivity. In substudies of Smart Mums with Smart Phones 2 (SMs2), a randomised controlled trial of a postpartum lifestyle intervention using text messages and an activity monitor in women with GDM, this thesis explores the effect of sleep and exercise on glycaemic control in women with GDM during pregnancy; the relationship of sleep on exercise and weight in postpartum women with GDM and investigates the impact of COVID lockdown on physical activity of postpartum women. Key findings include a trend towards improved postprandial blood glucose levels with increased steps during pregnancy; achieving healthy sleep targets during pregnancy improved the likelihood of reaching glycaemic targets; increased sleep duration after pregnancy was associated with more steps being taken; positive relationship between sleep and postpartum weight; postpartum physical activity paradoxically increased during COVID lockdown. This thesis provides further evidence of a relationship between sleep, glycaemic control and diabetes risk. Sleep is an under-recognised risk factor that should be considered during GDM and post-partum. Activity monitors may optimise lifestyle interventions and support research data collection

Sedentary And Physical Activity Patterns In Adults With Intellectual Disability

Little is known about the patterns of sedentary time (ST) and physical activity (PA) levels throughout the week among adults and older adults with Intellectual Disability (ID). We analyzed ST and PA patterns of adults and older adults with ID. Forty-two adults and 42 older adults with mild to severe ID participated in this study. Height and weight were obtained to calculate Body Mass Index (BMI). Body fat and fat-free mass percentages were also obtained. Patterns of PA levels and ST were assessed with GT3X Actigraph accelerometers. Adults performed higher amounts of total PA and moderate to vigorous PA than older adults during the week, on weekdays and in center time (all p > 0.05). No differences between males and females were found for either PA levels or ST. Only 10.7% of the participants met the global recommendations on PA for health. The participants of the current study showed low PA levels and a high prevalence of ST. Interestingly, when comparing age and/or sex groups, no differences were observed for ST. Our findings provide novel and valuable information to be considered in future interventions aiming to increase PA levels and reduce ST