The Internet of Things (IoT) offers transformative potential when combined with Machine Learning (ML), but labeling diverse IoT data remains challenging. To address this, we introduce SenseScape Testbed, an IoT experimentation platform for indoor environments with wireless sensor nodes, robots, and location-tracking nodes. This testbed enables IoT applications such as human activity recognition and indoor mobility tracking, supporting energy efficiency, occupant comfort, and context representation while providing a versatile environment for labeling and testing to advance ML algorithms tailored for IoT applications.

Medication non-adherence is a major healthcare challenge globally, with over half of patients with chronic conditions in developed countries failing to follow their prescribed medication regimen. This can lead to poor disease outcomes, increased hospital visits, and a significant financial burden on healthcare systems [1]. These issues have driven a recent wave of research, including the development of smart adherence products [6] that can be incorporated into a patient’s daily life to monitor medication adherence. In this work, we present a radar-based system for user identification while taking medication, which extends our recent work [5]. we conducted preliminary experiments examining semi-medication-taking activities executed by 6 subjects. Our system achieved 80% accuracy in identifying who has taken the medication in a group of 3 subjects.

Medication adherence is one of the leading factors that can make the difference between life and death, especially for patients managing chronic conditions. These issues have driven a recent wave of research, including the development of smart pill bottles that monitor when a pill is extracted. The goal of my PhD research is to develop systems that can identify who has taken the pill and when. To do so, we have designed different generations of smart pill bottles and associated algorithms for enabling several applications. We use 3D-printed pill bottles equipped with a magnetic switch sensor and an accelerometer. The bottles are carefully designed to minimize power consumption and we devise new machine learning-based techniques that use the accelerometer data generated during bottle interaction (pill extraction) to capture the user gesture that is extracting the pill. Our work can be classified into 3 core thrust areas: 1) User identification using smart pill bottle systems. 2) Adaptive learning techniques for user identification across multiple smart pill bottles. 3) Latent conditions monitoring using smart pill bottles.

Medication adherence is one of the leading factors that can make the difference between life and death, especially for patients managing chronic conditions [2]. Indeed, these issues have driven a recent wave of research, including the development of smart pill bottles that monitor when a pill is extracted. In this poster, we extend our recent work [1], where we present adaptive learning techniques for subject identification across multiple pill bottle systems. We collect inertial signals from 10 subjects taking medication pills and encode the activity signals by transforming them into 2D texture images. Then we use pre-trained Convolutional Neural Network (CNN) models for image-based classification tasks. Our approach achieved improved differentiation capacity over existing models by using deep learning models, modified through domain adaptation and transfer learning.

The COVID-19 pandemic has impacted academic life in different ways. In the mobile and pervasive computing community, there was a struggle on data collection for the evaluation of human-sensing systems. An automated and contactless solution to collect data from users at home is one way that can help in the continuation of user-centric studies. In this poster, we present a portable system for remote, in-home data collection. The system is powered by a Raspberry Pi© and input peripherals (a camera, a microphone, and a wireless receiver). Our system uses a speech interface for text-to-speech and speech-to-text conversions. The system acts as a voice-based "smart agent" that guides the user during an experiment session. We aim to use our system to collect data from a set of smart pill bottles that we previously designed for medication adherence monitoring [1] and user identification [3].

The past 40 years have seen an explosion of Radio Frequency (RF) transmitters, which motivates understanding their impacts on the natural world. The European honeybee, Apis Mellifera, has been shown to sense the Earth's magnetic field. Human Radio Frequency (RF) transmitters alter this field. For example, recent work demonstrated that human-created RF interferes with the common robin's ability to orient themselves. This work proposes an experimental design to determine if honeybees can sense RF transmissions in frequencies from 1 MHz (AM radio) to 6 GHz (WiFi). We deployed a custom-designed RF bee feeder near bee hives to test honeybees' RF sensing ability.

Accurately accounting for medication use is important for the efficacy and safety of patients and family members. Monitoring is also important for medication adherence. This work investigates identification of persons taking medication using a sensor-equipped pill bottle. The bottle is equipped with inertial and switch sensors in both the cap and body, making the added hardware unobtrusive, low-cost, and wireless. Our system uses inertial data to build a patient discrimination model using classification techniques. We evaluated the system using 16 subjects. Our results show that using binary Support Vector Machine (SVM), the system can discriminate one patient among 16 subjects with 94% accuracy, and has a 93% using a single sensor. Identifying the exact person in a set of 3 subjects has an accuracy higher than 91%.

In this work, we investigate the identification of persons taking medication using a sensor-equipped pill-bottle. The bottle embeds inertial sensors in both the cap and body, making the added hardware un-obtrusive, low-cost, and wireless. Our system uses inertial data to build a patient discrimination model using classification techniques. We evaluated the system using 16 subjects. Our results show that using binary Support Vector Machine (SVM), the system can discriminate one patient among 16 subjects with 94 % accuracy. Identifying the exact person in a set of 3 subjects has an accuracy higher than 91 %..