TennisSense: a platform for extracting semantic information from multi-camera tennis data

In this paper, we introduce TennisSense, a technology platform for the digital capture, analysis and retrieval of tennis training and matches. Our algorithms for extracting useful metadata from the overhead court camera are described and evaluated. We track the tennis ball using motion images for ball candidate detection and then link ball candidates into locally linear tracks. From these tracks we can infer when serves and rallies take place. Using background subtraction and hysteresis-type blob tracking, we track the tennis players positions. The performance of both modules is evaluated using ground-truthed data. The extracted metadata provides valuable information for indexing and efficient browsing of hours of multi-camera tennis footage and we briefly illustrative how this data is used by our tennis-coach playback interface

Labeling of Activity Recognition Datasets: Detection of Misbehaving Users

Automatic recognition of user’s activities by means of wearable devices is a key element of many e-health applications, ranging from rehabilitation to monitoring of elderly citizens. Activity recognition methods generally rely on the availability of annotated training sets, where the traces collected using sensors are labelled with the real activity carried out by the user. We propose a method useful to automatically identify misbehaving users, i.e. the users that introduce inaccuracies during the labeling phase. The method is semi-supervised and detects misbehaving users as anomalies with respect to accurate ones. Experimental results show that misbehaving users can be detected with more than 99% accuracy

Continuous and automated data collection in migraine research - Extending the data collection capabilities of the Empatica E4

Migraine is a recurrent headache disorder that afflicts significant portions of the global population. There is no current cure and migraines are mainly managed through symptomatic medical treatments and manual biofeedback routines. Automated data collection and prediction of migraine attacks through machine learning could be viable approaches for helping migraineurs and for reducing the impact of migraines, both on a societal and an individual level. However, machine learning approaches require access to large amounts of high-quality real-time data for facilitating prompt and reliable prediction under everyday conditions and within useful timeframes. The Empatica E4 is an unobtrusive wearable sensor device that can satisfy these data collection needs, although not without flaws and shortcomings. Several studies have reported issues with E4 data collection, most regarding participant involvement and the logistical aspects of the collection process. On top of this, the native systems provided by Empatica for storing, retrieving, and utilizing collected data do not properly facilitate real-time data analysis or machine learning approaches. This project creates a flexible data collection solution based on the E4 for facilitating real-time prediction of migraine attacks. It incorporates features and elements for increasing user involvement and for maximizing the data collection potential of the E4. Additionally, the solution is integrated with the mSpider data storage platform, facilitating reliable and flexible data storage and retrieval options. The prototype system was tested on three potential end-users under everyday conditions over the course of 20 days. After the data collection period, each user attended a semi-structured interview. Testing and interview results show that the data collection capabilities of the prototype system are on-par with other similar systems, it offers stable data collection under everyday conditions, and it can store data in the mSpider system. However, the added features for increasing participant involvement had little discernible effect on the data collection process or the amount of collected data. This was probably caused by the low intensity of the added features or the short duration of the testing period. Additionally, the testing process found that the high technical proficiency requirements and the necessary daily maintenance of the E4 makes it unsuited for continuous migraine treatment purposes, although it is a good tool for migraine research. Future prototype iterations should increase the intensity of the participant involvement features and greatly increase the length of testing periods

Objective Measurement of Physician Stress in the Emergency Department Using a Wearable Sensor

Physician stress, and resultant consequences such as burnout, have become increasingly recognized pervasive problems, particularly within the specialty of Emergency Medicine. Stress is difficult to measure objectively, and research predominantly relies on self-reported measures. The present study aims to characterize digital biomarkers of stress as detected by a wearable sensor among Emergency Medicine physicians. Physiologic data was continuously collected using a wearable sensor during clinical work in the emergency department, and participants were asked to self-identify episodes of stress. Machine learning algorithms were used to classify self-reported episodes of stress. Comparing baseline sensor data to data in the 20-minute period preceding self-reported stress episodes demonstrated the highest prediction accuracy for stress. With further study, detection of stress via wearable sensors could be used to facilitate evidence-based stress research and just-in-time interventions for emergency physicians and other high-stress professionals