Table 3.
Summary of Google Glass approach as patient-centered interventions.
| Source (health condition) | Purpose | Intervention description |
| Anam et al, 2014 (Ophthalmology – visual impairment) |
To allow people with vision impairments gain the ability to determine non-verbal expressions | Expression is the type of feature addition that is being used |
| It analyzes changes in facial expression and relays that information in the form of captured frames to user | ||
| Helps user change their posture to better capture the facial expression | ||
| Garcia and Nahapetian, 2015 (Ophthalmology – visual impairment) |
To help guide people with visual impairments navigate indoor environments | Extract floor regions from images captured from GG to help guide the individual |
| An app is installed in GG that starts the camera and sends image frames to the mobile phone | ||
| An app is also installed that analyzes the floor plans and then sends it to the mobile phone through Bluetooth | ||
| Images that are captured contain the walls, floor, and ceiling | ||
| Pundlik et al, 2016 (Ophthalmology – visual impairment) |
To use vision magnification to aid in the completion of tasks | Leverages zoom capabilities of GG |
| Students are assigned tasks that involve the calculator and music player apps | ||
| Performance on these tasks is measured | ||
| Hwang and Peli, 2016 (Ophthalmology – advanced age-related macular degeneration) |
To augment the vision of the wearer so that they have improved vision | Vision enhancement tool is added to GG |
| Participant wears GG which now warps the camera image to improve vision | ||
| Images that the vision enhancement tool sees are then relayed to user in real-time | ||
| Tanuwidjaja et al, 2014 (Ophthalmology – colorblindness) |
To help people with colorblindness see color | Alters the way people perceive color |
| Applied Chroma, which is an app that detects color and relays that information to the participant | ||
| Implemented the Ishihara test, which tests for color vision deficiency | ||
| Implemented the Blackboard test that determines if a person can distinguish between green and orange | ||
| Lazewatsky et al, 2014 (Motor impairment) |
To show that GG can be used in conjunction with the PR2 robot to recognize people and objects and then manipulate the space around it | GG Bridge Node receives sensor data from GG and transmits it to Robots and Systems software (ROS) messages and publishes a coordinate frame for GG |
| ROS works with face detection; GG software also uses face detection and person recognition | ||
| Gips et al, 2015 (Motor impairment) |
To help people operate a computer with only eye or head movements | Noggin software was developed to allow user to move a cursor across the screen through head movements |
| Noggin displays yes, no, and enter on the screen | ||
| Noggin uses the gyroscope to monitor head movements | ||
| GG Gab, another software, allows user to spell out a message | ||
| Sinyukov et al, 2016 (Motor impairment – Locked-In Syndrome) |
To help patients have better control over their wheelchairs | Patient uses the software installed on GG in conjunction with the motorized wheelchair |
| GG monitors facial expressions of the patient | ||
| GG’s audio monitoring is used to understand voice commands and then relay the instructions to the motorized wheelchair | ||
| Malu and Findlater, 2015 (Motor impairment – upper body) |
To assess the accessibility of GG for individuals with upper body motor impairments | Using voice commands and the touchpad to go through day-to-day activities |
| Touchpad on GG was on the right arm of the device and senses taps and swipes through voice commands | ||
| Output is projected on the heads-up display | ||
| Participants completed tasks using swipes and tasks function | ||
| Participants then used a scale to rate the comport and ease of the touchpad and visual display | ||
| McNaney et al, 2014 (Motor impairment – Parkinson’s Disease [PD]) |
To help people with PD counteract their symptoms by allowing them to carry out the normal functions of a mobile phone using voice commands, cueing for freezing gait | GG was used to manage social cues and alert the user |
| GG monitored movement and told the participant when they were freezing so that they could actively try to stop the behavior | ||
| McNaney et al, 2015 (Motor impairment – PD) |
To help monitor speech loudness issues and provide feedback to help with self-management | Developed the LApp app that monitors loudness |
| Participants used the app for a set amount of time while carrying out a series of social interactions | ||
| Indicating when the volume was inappropriate so the user could adjust to hit the target loudness | ||
| Zhao et al, 2016 (Motor impairment – PD) |
To provide visual and auditory cues to aid in the modulation of gait | GG was used to detect gait issues and improve them through cueing |
| Audiovisual cues were used, including a metronome, flashing light, optic flow, and a control (no cue) | ||
| Participants underwent a series of walking tasks and their gait was then analyzed for stability and freezing | ||
| Pervaiz and Patel, 2014 (Motor impairment – Dysarthria) |
To help patients monitor their low volume in order to self-regulate and to provide clinicians with feedback to adjust therapy | Developed the SpeedOmeter software that compares vocal loudness to ambient noise |
| Provides feedback to user on their volume | ||
| System provides usage and performance history for user | ||
| Notifies patient of their volume so they can adjust | ||
| Miranda et al, 2014 (Psychological/Developmental – SAD) |
To assess the feasibility of using GG to monitor blinking rates in individuals with social anxiety disorder | Monitor blinking behaviors |
| Used to gather data from the infrared (IR) sensor | ||
| The app dealt with IR data gathering, data processing, and HTTP communication | ||
| App processes the data and calculates when the user blinked | ||
| Voss et al, 2016 (Psychological/Developmental – ASD) |
To monitor life activities and allow for analysis of autism behaviors | Participant uses GG to record everyday behaviors |
| Caregiver reviews system highlights and emotional moments so they are easily accessible for the reviewer | ||
| Caregivers can tag parts of the video that are especially important and add comments to the video | ||
| Mirtchouk et al, 2016 (Eating monitoring) |
To accurately track an individual’s eating habits and provide feedback to help with self-regulation | GG sensor was used to detect head movement that was specific to eating |
| Participants ate what they wanted and when they wanted and GG was supposed to detect when they were eating and for how long | ||
| Participants were allowed to do other activities when eating their meals | ||
| Rahman et al, 2015 (Eating monitoring) |
To detect a person’s eating and drinking habits | Records a person’s eating and drinking habits through head movements |
| Helps people with obesity and diabetes | ||
| Developed the Glass Eating and Motion (GLEAM) dataset | ||
| Participants ate, walked, and did other activities during the monitoring period | ||
| Participants did not interact with GG but simply wore it | ||
| GG sensors recorded movement | ||
| Ye et al, 2015 (Eating monitoring) |
To detail eating habits to help weight reduction | Collects images of the person’s day from their perspective every 30 seconds |
| Amazon’s Mechanical Turk is a human computation platform that can determine eating behaviors and is used to identify when a person is eating | ||
| Hernandez et al, 2014 (Physiological measurements) |
To measure heart rate and breaths per minute | Participant would wear GG, and GG’s accelerometer, gyroscope, and camera were used to find user’s pulse and respiratory rates |
| The recording was done in several different positions including, sitting, standing, and lying down | ||
| Richer et al, 2015 (Physiological measurements) |
To use the DailyHeart app to monitor ECGs | GG presents ECG signals to user in everyday life |
| Signals are processed in real-time and classify the user’s heart beats | ||
| It will store data in an internal database | ||
| Wiesner et al, 2015 (Allergies) |
To give consumers information of possible allergens in cosmetic products | An app is developed for GG whose purpose is to scan products |
| User scans the product in the store and the GG app identifies the product | ||
| User has uploaded the information of their specific allergies and the app compares the ingredients to the user’s profile | ||
| GG indicates whether the user should buy the product and why |