Google Glass is a recently designed wearable device capable of displaying information in a smartphone-like hands-free format by wireless communication. The Glass also provides convenient control over remote devices, primarily enabled by voice recognition commands. These unique features of the Google Glass make it useful for medical and biomedical applications where hands-free experiences are strongly preferred. Here, we report for the first time, an integral set of hardware, firmware, software, and Glassware that enabled wireless transmission of sensor data onto the Google Glass for on-demand data visualization and real-time analysis. Additionally, the platform allowed the user to control outputs entered through the Glass, therefore achieving bi-directional Glass-device interfacing. Using this versatile platform, we demonstrated its capability in monitoring physical and physiological parameters such as temperature, pH, and morphology of liver- and heart-on-chips. Furthermore, we showed the capability to remotely introduce pharmaceutical compounds into a microfluidic human primary liver bioreactor at desired time points while monitoring their effects through the Glass. We believe that such an innovative platform, along with its concept, has set up a premise in wearable monitoring and controlling technology for a wide variety of applications in biomedicine.
Medical devices increasingly depend on computing functions such as wireless communication and Internet connectivity for software-based control of therapies and network-based transmission of patients' stored medical information. These computing capabilities introduce security and privacy risks, yet little is known about the prevalence of such risks within the clinical setting.
Parkinson’s disease (PD) is a slowly progressing neurodegenerative disease with early manifestation of motor signs. Objective measurements of motor signs are of vital importance for diagnosing, monitoring and developing disease modifying therapies, particularly for the early stages of the disease when putative neuroprotective treatments could stop neurodegeneration. Current medical practice has limited tools to routinely monitor PD motor signs with enough frequency and without undue burden for patients and the healthcare system. In this paper, we present data indicating that the routine interaction with computer keyboards can be used to detect motor signs in the early stages of PD. We explore a solution that measures the key hold times (the time required to press and release a key) during the normal use of a computer without any change in hardware and converts it to a PD motor index. This is achieved by the automatic discovery of patterns in the time series of key hold times using an ensemble regression algorithm. This new approach discriminated early PD groups from controls with an AUC = 0.81 (n = 42/43; mean age = 59.0/60.1; women = 43%/60%;PD/controls). The performance was comparable or better than two other quantitative motor performance tests used clinically: alternating finger tapping (AUC = 0.75) and single key tapping (AUC = 0.61).
Identifying the true identity of a subject in the absence of external verification criteria (documents, DNA, fingerprints, etc.) is an unresolved issue. Here, we report an experiment on the verification of fake identities, identified by means of their specific keystroke dynamics as analysed in their written response using a computer keyboard. Results indicate that keystroke analysis can distinguish liars from truth tellers with a high degree of accuracy - around 95% - thanks to the use of unexpected questions that efficiently facilitate the emergence of deception clues.
To evaluate the efficacy of a portable, wearable, wireless artificial pancreas system (the Diabetes Assistant [DiAs] running the Unified Safety System) on glucose control at home in overnight-only and 24/7 closed-loop control (CLC) modes in patients with type 1 diabetes.
The global epidemic of diabetes calls for innovative interventions. This study evaluated the effectiveness of the Project Dulce model, with and without wireless technology, on glycemic control and other clinical and self-reported outcomes in patients with poorly controlled type 2 diabetes in Mexico.
Different (Key)Strokes for Different Folks: How Standard and Nonstandard Typists Balance Fitts' Law and Hick’s Law
- Journal of experimental psychology. Human perception and performance
- Published almost 4 years ago
Fine motor skills like typing involve a mapping problem that trades Fitts' law against Hick’s law. Eight fingers have to be mapped onto 26 keys. Movement time increases with distance, so Fitts' law is optimized by recruiting more fingers. Choice difficulty increases with the number of alternatives, so Hick’s law is optimized by recruiting fewer fingers. The effect of the number of alternatives decreases with consistent practice, so skilled typists achieve a balance between Fitts' law and Hick’s law through learning. We tested this hypothesis by comparing standard typists who use the standard QWERTY mapping consistently with nonstandard typists who use fewer fingers less consistently. Typing speed and accuracy were lower for nonstandard typists, especially when visual guidance was reduced by removing the letters from the keys or covering the keyboard. Regression analyses showed that accommodation to Fitts' law (number of fingers) and Hick’s law (consistency) predicted typing speed and accuracy. We measured the automaticity of typing in both groups, testing for hierarchical control in 3 tasks: word priming, which measures parallel activation of keystrokes, keyboard recall, which measures explicit knowledge of letter locations, and hand cuing, which measures explicit knowledge of which hand types which letter. Standard and nonstandard typists showed similar degrees of hierarchical control in all 3 tasks, suggesting that nonstandard typists type as automatically as standard typists, but their suboptimal balance between Fitts' law and Hick’s law limits their ability to type quickly and accurately. (PsycINFO Database Record
Modern digital devices and appliances are capable of monitoring the timing of button presses, or finger interactions in general, with a sub-millisecond accuracy. However, the massive amount of high resolution temporal information that these devices could collect is currently being discarded. Multiple studies have shown that the act of pressing a button triggers well defined brain areas which are known to be affected by motor-compromised conditions. In this study, we demonstrate that the daily interaction with a computer keyboard can be employed as means to observe and potentially quantify psychomotor impairment. We induced a psychomotor impairment via a sleep inertia paradigm in 14 healthy subjects, which is detected by our classifier with an Area Under the ROC Curve (AUC) of 0.93/0.91. The detection relies on novel features derived from key-hold times acquired on standard computer keyboards during an uncontrolled typing task. These features correlate with the progression to psychomotor impairment (p < 0.001) regardless of the content and language of the text typed, and perform consistently with different keyboards. The ability to acquire longitudinal measurements of subtle motor changes from a digital device without altering its functionality may allow for early screening and follow-up of motor-compromised neurodegenerative conditions, psychological disorders or intoxication at a negligible cost in the general population.
A goal of brain-computer interface research is to develop fast and reliable means of communication for individuals with paralysis and anarthria. We evaluated the ability of an individual with incomplete locked-in syndrome enrolled in the BrainGate Neural Interface System pilot clinical trial to communicate using neural point-and-click control. A general-purpose interface was developed to provide control of a computer cursor in tandem with one of two on-screen virtual keyboards. The novel BrainGate Radial Keyboard was compared to a standard QWERTY keyboard in a balanced copy-spelling task. The Radial Keyboard yielded a significant improvement in typing accuracy and speed-enabling typing rates over 10 correct characters per minute. The participant used this interface to communicate face-to-face with research staff by using text-to-speech conversion, and remotely using an internet chat application. This study demonstrates the first use of an intracortical brain-computer interface for neural point-and-click communication by an individual with incomplete locked-in syndrome.
The emergence of wireless technologies such as WirelessHART and ISA100 Wireless for deployment at industrial process plants has urged the need for research and development in wireless control. This is in view of the fact that the recent application is mainly in monitoring domain due to lack of confidence in control aspect. WirelessHART has an edge over its counterpart as it is based on the successful Wired HART protocol with over 30 million devices as of 2009. Recent works on control have primarily focused on maintaining the traditional PID control structure which is proven not adequate for the wireless environment. In contrast, Internal Model Control (IMC), a promising technique for delay compensation, disturbance rejection and setpoint tracking has not been investigated in the context of WirelessHART. Therefore, this paper discusses the control design using IMC approach with a focus on wireless processes. The simulation and experimental results using real-time WirelessHART hardware-in-the-loop simulator (WH-HILS) indicate that the proposed approach is more robust to delay variation of the network than the PID.