Background With nearly 20% of the US adult population using fitness trackers, there is an increasing focus on how physiological data from these devices can provide actionable insights about workplace performance. However, in-the-wild studies that understand how these metrics correlate with cognitive performance measures across a diverse population are lacking, and claims made by device manufacturers are vague. While there has been extensive research leading to a variety of theories on how physiological measures affect cognitive performance, virtually all such studies have been conducted in highly controlled settings and their validity in the real world is poorly understood. Objective We seek to bridge this gap by evaluating prevailing theories on the effects of a variety of sleep, activity, and heart rate parameters on cognitive performance against data collected in real-world settings. Methods We used a Fitbit Charge 3 and a smartphone app to collect different physiological and neurobehavioral task data, respectively, as part of our 6-week-long in-the-wild study. We collected data from 24 participants across multiple population groups (shift workers, regular workers, and graduate students) on different performance measures (vigilant attention and cognitive throughput). Simultaneously, we used a fitness tracker to unobtrusively obtain physiological measures that could influence these performancemore »
AlertnessScanner: what do your pupils tell about your alertness
Alertness is a crucial component of our cognitive performance. Reduced alertness can negatively impact memory consolidation, productivity and safety. As a result, there has been an increasing focus on continuous assessment of alertness. The existing methods usually require users to wear sensors, fill out questionnaires, or perform response time tests periodically, in order to track their alertness. These methods may be obtrusvie to some users, and thus have limited capability. In this work, we propose AlertnessScanner, a computer-vision-based system that collects in-situ pupil information to model alertness in the wild. We conducted two in-the-wild studies to evaluate the effectiveness of our solution, and found that AlertnessScanner passively and unobtrusively assess alertness. We discuss the implications of our findings and present opportunities for mobile applications that measure and act upon changes in alertness.
- Award ID(s):
- 1840025
- Publication Date:
- NSF-PAR ID:
- 10113342
- Journal Name:
- MobileHCI '18 Proceedings of the 20th International Conference on Human-Computer Interaction with Mobile Devices and Services
- Page Range or eLocation-ID:
- 1 to 11
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
Background Inhibitory control, or inhibition, is one of the core executive functions of humans. It contributes to our attention, performance, and physical and mental well-being. Our inhibitory control is modulated by various factors and therefore fluctuates over time. Being able to continuously and unobtrusively assess our inhibitory control and understand the mediating factors may allow us to design intelligent systems that help manage our inhibitory control and ultimately our well-being. Objective The aim of this study is to investigate whether we can assess individuals’ inhibitory control using an unobtrusive and scalable approach to identify digital markers that are predictive of changes in inhibitory control. Methods We developed InhibiSense, an app that passively collects the following information: users’ behaviors based on their phone use and sensor data, the ground truths of their inhibition control measured with stop-signal tasks (SSTs) and ecological momentary assessments (EMAs), and heart rate information transmitted from a wearable heart rate monitor (Polar H10). We conducted a 4-week in-the-wild study, where participants were asked to install InhibiSense on their phone and wear a Polar H10. We used generalized estimating equation (GEE) and gradient boosting tree models fitted with features extracted from participants’ phone use and sensor data tomore »
-
Embedded database libraries provide developers with a com- mon and convenient data persistence layer. They have spread to many systems, including interactive devices like smart- phones, appearing in all major mobile systems. Their perfor- mance affects the response times and resource consumption of millions of phone apps and billions of phone users. It is thus critical that we better understand how they work, so they can be used more efficiently, and so developers can make faster libraries. Mobile databases differ significantly from server-class storage in terms of platform, usage, and measurement. Phones are multi-tenant, end-user devices that the database must share with other apps. Contrary to traditional database design goals, workloads on phones are single-app, bursty, and rarely saturate the CPU. We argue that mobile storage design should refocus on what matters on the mobile platform: latency and energy. As accurate per- formance measurement tools are necessary to evaluation of good database design, this uncovers another issue: Tradi- tional database benchmarking methods produce misleading results when applied to mobile devices, due to evaluating performance at saturation. Development of databases and measurements specifically designed for the mobile platform is necessary to optimize user experience of the most common database usage inmore »
-
Traditional Internet routing is simple, scalable and robust, but cannot provide perfect QoS support due to the current completely distributed hop-by-hop routing architecture. Software defined networking (SDN) opens up the door to traffic engineering innovation and makes possible QoS routing with a broader picture of overall network resources. We further argue that SDN can provide more opportunity for the network users to make their own routing selections with network programmability. In this paper, we propose OpenMCR, a general framework for network users to make their own choice of routing given various requirements. OpenMCR provides routing subject to several additive QoS constraints, which is NP-hard when the number of constraints is two or more. By composing various necessary conditions with different path extension schemes, our platform can customize routing solutions for each network user based on their own requirements. Through experiments in an SDN emulated environment, we evaluate multiple aspects of OpenMCR, demonstrate its effectiveness compared with several baselines and validate our theoretical analysis.
-
Although driving is a complex and multitask activity, it is not unusual for drivers to engage simultaneously in other non-driving related tasks using secondary in-vehicle displays (IVIS). The use of IVIS and its potential negative safety consequences has been investigated over the years. However with the advent and advance of in-vehicle technologies such as augmented-reality head-up displays (AR HUDs), there are increasing opportunities for improving secondary task engagement and decreasing negative safety consequences. In this study, we aim to understand the effects of AR HUD low cognitive load tasks on driving performance during monotonous driving. Adapting NHTSA’s driver distraction guidelines, we conducted a user-study with twenty-four gender-balanced participants that performed secondary AR HUD tasks of different durations while driving in a monotonous environment using a medium-fidelity driving simulator. We performed a mixed-methods analysis to evaluate driver’s perceived workload (NASA-TLX), lateral, and longitudinal driving performance. Although we found that drivers subjectively perceive AR HUD tasks to have a higher cognitive demand; AR tasks resulted in improved driving performance. Conversely, the duration of the secondary tasks had no measurable impacts on performance which suggests that the amount of time spent on tasks has no negative or positive implications on driving performance. Wemore »
- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Publisher's Version of Record
- https://doi.org/10.1145/3229434.3229456
