Jeffrey Lockhart

Lockhart, Jeffrey W. 2020. “‘A Large and Longstanding Body’: Historical Authority in the Science of Sex.” in Far-Right Revisionism and the End of History | Alt/Histories. ed. Louie Dean Valencia-García. New York: Routledge.

Recent improvements in data collection volume from planetary and space physics missions have allowed the application of novel data science techniques. The Cassini mission for example collected over 600 gigabytes of scientific data from 2004 to 2017. This represents a surge of data on the Saturn system. In comparison, the previous mission to Saturn, Voyager over 20 years earlier, had onboard a ~70 kB 8-track storage ability. Machine learning can help scientists work with data on this larger scale. Unlike many applications of machine learning, a primary use in planetary space physics applications is to infer behavior about the system itself. This raises three concerns: first, the performance of the machine learning model, second, the need for interpretable applications to answer scientific questions, and third, how characteristics of spacecraft data change these applications. In comparison to these concerns, uses of “black box” or un-interpretable machine learning methods tend toward evaluations of performance only either ignoring the underlying physical process or, less often, providing misleading explanations for it. The present work uses Cassini data as a case study as these data are similar to space physics and planetary missions at Earth and other solar system objects. We build off a previous effort applying a semi-supervised physics-based classification of plasma instabilities in Saturn’s magnetic environment, or magnetosphere. We then use this previous effort in comparison to other machine learning classifiers with varying data size access, and physical information access. We show that incorporating knowledge of these orbiting spacecraft data characteristics improves the performance and interpretability of machine leaning methods, which is essential for deriving scientific meaning. Building on these findings, we present a framework on incorporating physics knowledge into machine learning problems targeting semi-supervised classification for space physics data in planetary environments. These findings present a path forward for incorporating physical knowledge into space physics and planetary mission data analyses for scientific discovery.

Azari, Abigail R., Jeffrey W. Lockhart, Michael W. Liemohn, and Xianzhe Jia. 2020. Incorporating Physical Knowledge into Machine Learning for Planetary Space Physics. Frontiers in Astronomy and Space Sciences. arXiv

Actitracker is a smartphone-based activity-monitoring service to help people ensure they receive sufficient activity to maintain proper health. This free service allowed people to set personal activity goals and monitor their progress toward these goals. Actitracker uses machine learning methods to recognize a user’s activities. It initially employs a “universal” model generated from labeled activity data from a panel of users, but will automatically shift to a much more accurate personalized model once a user completes a simple training phase. Detailed activity reports and statistics are maintained and provided to the user. Actitracker is a research-based system that began in 2011, before fitness trackers like Fitbit were popular, and was deployed for public use from 2012 until 2015, during which period it had 1,000 registered users. This paper describes the Actitracker system, its use of machine learning, and user experiences. While activity recognition has now entered the mainstream, this paper provides insights into applied activity recognition, something that commercial companies rarely share.

Weiss, Gary M., Jeffrey W. Lockhart, Tony T. Pulickal, Paul T. McHugh, Isaac H. Ronan, and Jessica L. Timko. 2016. “Actitracker: A Smartphone-based Activity Recognition System for Improving Health and Well-Being.” in Proceedings of the 3rd IEEE International Conference on Data Scienceand Advanced Analytics (DSAA). Montreal.

Human activity recognition (AR) has begun to mature as a field, but for AR research to thrive, large, diverse, high quality, AR data sets must be publically available and AR methodology must be clearly documented and standardized. In the process of comparing our AR research to other efforts, however, we found that most AR data sets are sufficiently limited as to impact the reliability of existing research results, and that many AR research papers do not clearly document their experimental methodology and often make unrealistic assumptions. In this paper we outline problems and limitations with AR data sets and describe the methodology problems we noticed, in the hope that this will lead to the creation of improved and better documented data sets and improved AR experimental methodology. Although we cover a broad array of methodological issues, our primary focus is on an often overlooked factor, model type, which determines how AR training and test data are partitioned—and how AR models are evaluated. Our prior research indicates that personal, hybrid, and impersonal/universal models yield dramatically different performance [30], yet many research studies do not highlight or even identify this factor. We make concrete recommendations to address these issues and also describe our own publically available AR data sets.

Jeffrey W. Lockhart, Gary M. Weiss, “Limitations with Activity Recognition Methodology & Data Sets,” Proceedings of the ACM International Joint Conference on Pervasive and Ubiquitous Computing, Adjunct Publication, Seattle, WA (2014): 747-756.

This paper describes Actitracker, a smartphone-based activitymonitoring service that helps people ensure that they receive sufficient activity to maintain proper health. Unlike most other such services, Actitracker requires only a smartphone (no watches, bands, or clip-on devices). This free service allows people to set personal activity goals and monitor their progress toward these  goals. Actitracker uses data mining to generate its activity recognition models. It initially uses a universal/impersonal model that is generated from labeled activity data from a panel of users, but will automatically generate, and deploy, much more accurate personalized models once a user completes a simple training phase. Detailed activity reports and statistics are maintained on the Actitracker server and are available to the user via a secure web interface. Actitracker has been deployed for several months and currently has over 250 registered users. This paper discusses user experiences with the service, as well as challenges and tradeoffs associated with building and deploying the service

Jeffrey W. Lockhart, Gary M. Weiss, “The Benefits of Personalized Models for Smartphone-Based Activity Recognition,” Proceedings of the SIAM International Conference on Data Mining, Philadelphia, PA (2014).

Technological advancements are leading to the emergence of wearable computing devices as a major consumer category. Several companies have developed, or are developing, wearable accessories to monitor human activity. But the health and wellness applications associated with these accessories can also benefit non-humans, and wearable computing accessories with such apps are now emerging for the pet market. In this paper we describe WagTag, an accessory that can be attached to a dog collar to track a dog’s activities and the intensity of these activities. The activity information is visually displayed on the device, while more detailed information can be uploaded to a computer via a Bluetooth connection. We describe key design issues and goals associated with the development of this device, especially with respect to aesthetics, durability, and functionality, and also describe WagTag’s prototype activity recognition models.

Gary M. Weiss, Ashwin Nathan, J.B. Kropp, Jeffrey W. Lockhart, “WagTag™: A Dog Collar Accessory for Monitoring Canine Activity Levels,” Proceedings of the Second Workshop on Wearable AR Systems for Industrial Applications, Zurich, Switzerland (at UbiComp 2013).

Activity Recognition (AR), which identifies the activity that a user performs, is attracting a tremendous amount of attention, especially with the recent explosion of smart mobile devices. These ubiquitous mobile devices, most notably but not exclusively smartphones, provide the sensors, processing, and communication capabilities that enable the development of diverse and innovative activity recognitionbased applications. However, although there has been a great deal of research into activity recognition, surprisingly little practical work has been done in the area of applications in mobile devices. In this paper we describe and categorize a variety of activity recognition-based applications. Our hope is that this work will encourage the development of such applications and also influence the direction of activity recognition research.

Jeffrey W. Lockhart, Tony T. Pulickal, Gary M. Weiss, “Applications of Mobile Activity Recognition,” Proceedings of the International Workshop on Situation, Activity and Goal Awareness, Pittsburgh, PA (at UbiComp 2012).

Mobile devices such as smart phones, tablet computers, and music players are ubiquitous. These devices typically contain many sensors, such as vision sensors (cameras), audio sensors (microphones), acceleration sensors (accelerometers) and location sensors (e.g., GPS), and also have some capability to send and receive data wirelessly. Sensor arrays on these mobile devices make innovative applications possible, especially when data mining is applied to the sensor data. But a key design decision is how best to distribute the responsibilities between the client (e.g., smartphone) and any servers. In this paper we investigate alternative architectures, ranging from a “dumb” client, where virtually all processing takes place on the server, to a “smart” client, where no server is needed. We describe the advantages and disadvantages of these alternative architectures and describe under what circumstances each is most appropriate. We use our own WISDM (WIreless Sensor Data Mining) architecture to provide concrete examples of the various alternatives.

Gary M. Weiss, Jeffrey W. Lockhart, “A Comparison of Alternative Client/Server Architectures for Ubiquitous Mobile Sensor-Based Applications,” Proceedings of the 1st International Workshop on Ubiquitous Mobile Instrumentation, Pittsburgh, PA (at UbiComp 2012).

Smartphones incorporate many diverse and powerful sensors, which creates exciting new opportunities for data mining and human-computer interaction. In this paper we show how standard classification algorithms can use labeled smartphone-based accelerometer data to identify the physical activity a user is performing. Our main focus is on evaluating the relative performance of impersonal and personal activity recognition models. Our impersonal (i.e., universal) models are built using training data from a panel of users and are then applied to new users, while our personal models are built with data from each user and then applied only to new data from that user. Our results indicate that the personal models perform dramatically better than the impersonal models—even when trained from only a few minutes worth of data. These personal models typically even outperform hybrid models that utilize both personal and impersonal data. These results strongly argue for the construction of personal models whenever possible. Our research means that we can unobtrusively gain useful knowledge about the habits of potentially millions of users. It also means that we can facilitate human computer interaction by enabling the smartphone to consider context and this can lead to new and more effective applications.

Gary M. Weiss, Jeffrey W. Lockhart, “The Impact of Personalization on Smartphone-Based Activity Recognition,” Proceedings of the Activity Context Representation Workshop, Toronto, Canada (at AAAI-2012).

Smart phones comprise a large and rapidly growing market. These devices provide unprecedented opportunities for sensor mining since they include a large variety of sensors, including an: acceleration sensor (accelerometer), location sensor (GPS), direction sensor (compass), audio sensor (microphone), image sensor (camera), proximity sensor, light sensor, and temperature sensor. Combined with the ubiquity and portability of these devices, these sensors provide us with an unprecedented view into people’s lives—and an excellent opportunity for data mining. But there are obstacles to sensor mining applications, due to the severe resource limitations (e.g., power, memory, bandwidth) faced by mobile devices. In this paper we discuss these limitations, their impact, and propose a solution based on our WISDM (WIireless Sensor Data Mining) smart phone-based sensor mining architecture.

Jeffrey W. Lockhart, Gary M. Weiss, Jack C. Xue, Shaun T. Gallagher, Andrew B. Grosner, and Tony T. Pulickal, “Design Considerations for the WISDM Smart Phone-Based Sensor Mining Architecture,” Proceedings of the Fifth International Workshop on Knowledge Discovery from Sensor Data, San Diego, CA (at KDD-2011): 25-33.