This paper describes the design and findings from a 15 week after-school program offered to middle-school students that covered a variety of computer science and engineering topics. Students in the program completed a short survey before and after the program conclusion that measured their sense of belonging, confidence, interest, and more in both fields. Throughout the program, students learned and applied skills in emerging areas in both computer science and engineering. Students then applied these skills to create a micro air-quality sensor that would allow their community to have a better understanding of their local air quality. Our initial quantitative results from the survey indicate little impact on the students. However, when examining the open questions from the students, it is more clear that there was indeed a positive impact with students. We also describe our experiences in providing the program and what changes we would make for future offerings.

This paper presents OMSense, an intelligent surface solution that leverages origami-inspired metasurfaces to allow scalable and precise surface pressure sensing. People interact with various surfaces daily, and these interactions cause the surfaces to deform, a process that can be captured by sensors. This interaction can be utilized in various forms of human-computer interaction and human monitoring, enabling new use cases. However, existing surface sensing schemes are either expensive, difficult to scale, or low-precision due to signal leakage in multiplex design. To solve this problem, we propose OMSense, which adopts the multiplex matrix sensing design and incorporates a 3D metastructure to reduce the shared physical connection-induced signal leakage. In addition to this physical augmentation, OMSense adopts a circuit-guided CNN to mitigate the circuit connection-induced signal leakage (ghosting). We 3D print a circuit-integrated metastructure and evaluate the sensor unit accuracy. OMSense achieves up to 2× sensor unit activation detection F1 score compared to the baselines.

This paper presents preliminary findings from a deployment of an Internet of Things (IoT) air quality sensor that can provide realtime and historical fine-grained air quality data. Current air quality data provided to communities may be inaccurate meaning that people are breathing air that is more polluted than what is reported. Through this research, we share our findings from testing in a controlled environment, a test deployment in an urban environment, and an enclosure for future deployments. From our test deployment we are able to determine that there is a significant difference between two locations that are less than 4 miles apart where the average particulate matter reading was more than double. These findings show that fine-grained air quality monitoring can provide the community with more meaningful data of the air they breathe.

We present a passive and non-intrusive sensing system for monitoring hand washing activity using structural vibration sensing. Proper hand washing is one of the most effective ways to limit the spread and transmission of disease, and has been especially critical during the COVID-19 pandemic. Prior approaches include direct observation and sensing-based approaches, but are limited in non-clinical settings due to operational restrictions and privacy concerns in sensitive areas such as restrooms. Our work introduces a new sensing modality for hand washing monitoring, which measures hand washing activity-induced vibration responses of sink structures, and uses those responses to monitor the presence and duration of hand washing. Primary research challenges are that vibration responses are similar for different activities, occur on different surfaces/structures, and tend to overlap/coincide. We overcome these challenges by extracting information about signal periodicity for similar activities through cepstrum-based features, leveraging hierarchical learning to differentiate activities on different surfaces, and denoting “primary/secondary” activities based on their relative frequency and importance. We evaluate our approach using real-world hand washing data across four different sink structures/locations, and achieve an average F1-score for hand washing activities of 0.95, which represents an 8.8X and 10.2X reduction in error over two different baseline approaches.

Detecting a previously unknown person (newcomer detection) is critical for visitor management, intruder prevention, and access control in smart buildings. Biometrics have been used to detect newcomers, including face, fingerprint, voice, iris, etc. These approaches often require active participation of the users or require dense instrumentation. Prior work using footstep-induced floor vibration to identify people removes these requirements, but only functions for known people due to the high variability in footstep-induced vibrations and the limited number of predicted classes in supervised learning. To overcome the limitations, we introduce a newcomer detection system based on non-parametric Bayesian learning, which models the variability and distribution of consecutive footstep-induced floor vibration for newcomer walking patterns. Preliminary results from real-world experiments with 6 people show up to 92% accuracy in newcomer detection with an average of 4 consecutive footsteps.

Gait health monitoring is critical for condition diagnosis and fall prediction in elderly populations. Existing methods for gait health monitoring (e.g. direct observation and sensing) are not suitable for non-clinical environments due to qualitative assessments or operational limitations. Our method utilizes footstep-induced floor vibration sensing to provide a passive gait health monitoring platform that can be used in non-clinical environments (e.g. home settings) to provide gait health information in a timely manner. We decompose vibration responses to obtain signal peaks that correspond to temporal gait information and leverage foot dominance to learn a signal amplitude-footstep ground reaction force transfer function. Preliminary results show that temporal gait parameters can be estimated with up to 99% accuracy and gait balance symmetry can be estimated with as low as 10.4% error.

Deep learning models have been successfully used in medical image analysis problems but they require a large amount of labeled images to obtain good performance. However, such large labeled datasets are costly to acquire. Active learning techniques can be used to minimize the number of required training labels while maximizing the model's performance. In this work, we propose a novel sampling method that queries the unlabeled examples that maximize the average distance to all training set examples in a learned feature space. We then extend our sampling method to define a better initial training set, without the need for a trained model, by using Oriented FAST and Rotated BRIEF (ORB) feature descriptors. We validate MedAL on 3 medical image datasets and show that our method is robust to different dataset properties. MedAL is also efficient, achieving 80% accuracy on the task of Diabetic Retinopathy detection using only 425 labeled images, corresponding to a 32% reduction in the number of required labeled examples compared to the standard uncertainty sampling technique, and a 40% reduction compared to random sampling.

No abstract available.

Touch surfaces are intuitive interfaces for computing devices. Most of the traditional touch interfaces (vision, IR, capacitive, etc.) have mounting requirements, resulting in specialized touch surfaces limited by their size, cost, and mobility. More recent work has shown that vibration-based touch sensing techniques can localize taps/knocks, which provides a low-cost flexible alternative. These surfaces are envisioned as intuitive inputs for applications such as interactive meeting tables, smart kitchen appliance control, etc. However, due to dispersive and reflective properties of various vibrating mediums, it is difficult to localize taps accurately on ubiquitous surfaces. Furthermore, no work has been done on tracking continuous swipe interactions through vibration sensing.