Inertial sensor based gait analysis discriminates subjects with and without visual impairment caused by simulated macular degeneration

Macular degeneration is the third leading cause of blindness worldwide and the leading cause of blindness in the developing world. The analysis of gait parameters can be used to assess the influence of macular degeneration on gait. This study examines the effect of macular degeneration on gait using inertial sensor based 3D spatio-temporal gait parameters. We acquired gait data from 21 young and healthy subjects during a 40 m obstacle walk. All subjects had to perform the gait trial with and without macular degeneration simulation glasses. The order of starting with or without glasses alternated between each subject in order to test for training effects. Multiple 3D spatio-temporal gait parameters were calculated for the normal vision as well as the impaired vision groups. The parameters trial time, stride time, stride time coefficient of variation (CV), stance time, stance time CV, stride length, cadence, gait velocity and angle at toe off showed statistically significant differences between the two groups. Training effects were visible for the trials which started without vision impairment. Inter-group differences in the gait pattern occurred due to an increased sense of insecurity related with the loss of visual acuity from the simulation glasses. In summary, we showed that 3D spatio-temporal gait parameters derived from inertial sensor data are viable to detect differences in the gait pattern of subjects with and without a macular degeneration simulation. We believe that this study provides the basis for an in-depth analysis regarding the impact of macular degeneration on gait.

[Advanced classification for cardiovascular devices]

Increasing complexity and many technical solutions for cardiovascular devices make it reasonable to introduce an advanced classification. The proposed classification has three levels. The first level distinguishes between implantable and wearable devices and the second level between therapeutic and diagnostic devices. The third level describes group-specific characteristics for each of the four subgroups. The aim of the classification is to differentiate the devices from each other and to easily describe interactions between two or more different devices. In addition, the classification should facilitate communication about these devices.

Comparision and analysis of top 10 exercise android Apps in mainland China

Medical guidelines highly recommend physical activity and aerobic exercise in the prevention of primary and secondary cardiovascular disease. The use of exercise-promoting application software may improve clinical outcomes for cardiovascular disease (CVD) patients. The study aimed to compare and analyze the functions of the top 10 exercise Android Apps which had more than 1,000,000 downloads from the main four Android App stores in mainland China. The results showed that most of these popular apps had pedometer, exercise plan preset, user data presentation, user encouragement and community sharing functions while a few of them had exercise video clips or animation support and wearable devices. Given these data, the conclusion is that these popular apps fulfill some of the functions recommended by medical guidelines, however, lack of some functions such as pre-exercise risk assessment, the exercise intensity recording, specific instructions by professionals, and monitoring functions for CVD patients.

Medical devices; general hospital and personal use monitoring devices; classification of the ingestible event marker. Final order

: The Food and Drug Administration (FDA) is classifying the ingestible event marker into class II (special controls). The Agency is classifying the device into class II (special controls) in order to provide a reasonable assurance of safety and effectiveness of the device.

Setting the scene: taxonomy of personal health monitoring

In order to provide a systematic basis for communication in trans-disciplinary research projects, there is a need for taxonomies and ontologies. Our developed taxonomy of personal health monitoring (PHM) is based on a systematic literature review and an iterative adaption process with trans-disciplinary partners. The construction method of the taxonomy is an ongoing process and need regularly updates.