Various commercial off-the-shelf components are available for the development of communication-enabled consumer electronics devices. This opens new doors to attackers who can take advantage of various vulnerabilities to attack the entire network and compromise the integrity of the system and the environment. If a malicious device enters the environment and the attacker gains access to the server or transmits malicious data to the server or cloud, the entire network can be jeopardized. To avoid such cases, this paper presents a device authentication scheme which uses Physical Unclonable Functions (PUFs) and is suitable for Internet-of-Medical-Things (IoMT). The main advantage of this authentication scheme is that no data related to the IoMT devices are stored in server memory. The time taken to authenticate the devices completely was 1.2 sec to 1.5 sec. A Hybrid Oscillator Arbiter Physical Unclonable Function was used for validation of the proposed authentication scheme. From the PUF module used during experimental validation, the number of keys that could be potentially used for the authentication protocol from each design is approximately 240. The proposed authentication scheme increases the robustness of the design while being lightweight to be deployed in various designs and supports scalability.

A correct balance of nutrient intake is very important, particularly in infants. When the body is deprived of essential nutrients, it can lead to serious disease and organ deterioration which can cause serious health issues in adulthood. Automated monitoring of the nutritional content of food provided to infants, not only at home but also in daycare facilities, is essential for their healthy development. To address this challenge, this paper presents a new Internet of Things (IoT)-based fully automated nutrition monitoring system, called Smart-Log, to advance the state-of-art in smart healthcare. For the realization of Smart-Log, a novel 5-layer perceptron neural network and a Bayesian network-based accurate meal prediction algorithm are presented in this paper. Smart-Log is prototyped as a consumer electronics product which consists of WiFi enabled sensors for food nutrition quantification, and a smart phone application that collects nutritional facts of the food ingredients. The Smart-Log prototype uses an open IoT platform for data analytics and storage. Experimental results consisting of 8172 food items for 1000 meals show that the prediction accuracy of Smart-Log is 98.6%.

Epilepsy, which is characterized by recurrent spontaneous seizures, has a considerably negative impact on both the quality and the expectancy of life of the patient. Approximately 3.4 million individuals in the USA and up to 1% of the world population are afflicted by epilepsy. This necessitates the real-time detection of seizures which can be done by the use of an IoT framework for smart healthcare. In this paper we propose an EEG based seizure detection system in the IoT framework which uses the discrete wavelet transform (DWT), Hjorth parameters (HPs), statistical features, and a machine learning classifier. Seizure detection is done in two stages. In the first stage, EEG signals are decomposed by the DWT into sub-bands and features (activity, signal complexity and standard deviation) were extracted from each of these sub-bands. In the second stage, a deep neural network (DNN) classifier is used to classify the EEG data. The prototype of the proposed Neuro-Detect was implemented using the hardware-in-the-loop approach. The results demonstrate a significant difference in HP values between interictal and ictal EEG with ictal EEG being less complex than interictal EEG. In this approach, we report an accuracy of 100% for a classification of normal vs. ictal EEG and 98.6% for normal and interictal vs. ictal EEG.

Considering today’s lifestyle, people just sleep forgetting the benefits sleep provides to the human body. Smart-Yoga Pillow (SaYoPillow) is proposed to help in understanding the relationship between stress and sleep and to fully materialize the idea of “Smart-Sleeping” by proposing an edge device. An edge processor with a model analyzing the physiological changes that occur during sleep along with the sleeping habits is proposed. Based on these changes during sleep, stress prediction for the following day is proposed. The secure transfer of the analyzed stress data along with the average physiological changes to the IoT cloud for storage is implemented. A secure transfer of any data from the cloud to any third party applications is also proposed. A user interface is provided allowing the user to control the data accessibility and visibility. SaYoPillow is novel, with security features as well as consideration of sleeping habits for stress reduction, with an accuracy of up to 96%.

Not knowing when to stop eating or how much food is too much can lead to many health issues. In iLog, we propose a system which can not only monitor but also create awareness for the user of how much food is too much. iLog provides information on the emotional state of a person along with the classification of eating behaviors to Normal-Eating or Stress-Eating. Chronic stress, uncontrolled or unmonitored food consumption, and obesity are intricately connected, even involving certain neurological adaptations. We propose a deep learning model for edge computing platforms which can automatically detect, classify and quantify the objects from the plate of the user. Three different paradigms where the idea of iLog can be performed are explored in this research. Two different edge platforms have been implemented in iLog. The platforms include mobile, as it is widely used, and a single board computer which can easily be a part of network for executing experiments with iLog-Glasses being the main wearable. The iLog model has produced an overall accuracy of 98% with an average precision of 85.8%.