We have released the video above which demonstrates the practical applications of our research in emergency care situations. In this case study, nurses or student nurses wearing a Smart-Assistant (in this example Smart-Eye-Ware) are attempting to resuscitate a patient (doll). Besides offering information on demand in their HMD (e.g. instant feedback, regulations or quick-check information, hints), the Smart-Assistant also detects specific activities like performing chest compressions and provides feedback if the activity is not performed to required standards.

This research expands on work presented in the award winning papers: Smart-Watch Life Saver: Smart-Watch Interactive-Feedback System for Improving Bystander CPR and Recognizing Hospital Care Activities with a Pocket Worn Smartphone (award details here).

Smart-Watch Life Saver:
Smart-Watch Interactive-Feedback System for Improving Bystander CPR

This potentially life saving technology, uses a smart-watch to unobtrusively guide its user in efficiently delivering CPR, and is SmartSociety’s first step in developing the Smart Nurse project.

The 19th International Symposium on Wearable Computers (ISWC 2015) was a conference dedicated to cutting-edge research in wearable technologies, and took place on 7-11 September in Osaka, Japan.

You can find the paper through our website, here, or in the conference proceedings, here.

Abstract: In this work a Smart-Watch application, that is able to monitor the frequency and depth of Cardiopulmonary Resuscitation (CPR) and provide interactive corrective feedback is described. We have evaluated the system with a total of 41 subjects who had undertaken a single episode of CPR training several years previously. This training was part of a First Aid course for lay people, commonly accessed in this population. The evaluation was conducted by measuring participant CPR competence using the “gold standard” of CPR training, namely frequency and compression depth. The evaluation demonstrated that the Smart Watch feedback system provided a significant improvement in the participant performance. For example, it doubled the number of people who could maintain both the parameters in the recommended range for at least 50% of the time.

doi: http://doi.acm.org/10.1145/2802083.2802086

Citation: Agnes Gruenerbl, Gerald Pirkl, Eloise Monger, Mary Gobbi, and Paul Lukowicz. 2015. Smart-watch life saver: smart-watch interactive-feedback system for improving bystander CPR. In Proceedings of the 2015 ACM International Symposium on Wearable Computers (ISWC ’15). ACM, New York, NY, USA, 19-26.

Citation: http://bit.ly/1NgCIlk

Our paper “Recognizing Hospital Care Activities with a Pocket Worn Smartphone” received the Best Paper Award at the 6th International Conference on Mobile Computing, Applications and Services (MobiCASE 2014).

This has been a joint effort of the German Research Centre for Artificial Intelligence and the University of Trento, and concerns the problem of “semantic gap” between machine and human semantics.

Abstract: In this work, we show how a smart-phone worn unobtrusively in a nurses coat pocket can be used to document the patient care activities performed during a regular morning routine. The main contribution is to show how, taking into account certain domain specific boundary conditions, a single sensor node worn in such an (from the sensing point of view) unfavorable location can still recognize complex, sometimes subtle activities. We evaluate our approach in a large real life dataset from day to day hospital operation. In total, 4 runs of patient care per day were collected for 14 days at a geriatric ward and annotated in high detail by following the performing nurses for the entire duration. This amounts to over 800 hours of sensor data including acceleration, gyroscope, compass, wifi and sound annotated with groundtruth at less than 1min resolution.

Index Terms: Activity Recognition, health care documentation, real-world study

Citation: Gernot Bahle, Agnes Gruenerbl, Enrico Bignotti, Mattia Zeni, Fausto Giunchiglia and Paul Lukowicz (2014): “Recognizing Hospital Care Activities with a Pocket Worn Smartphone”, 6th International Conference on Mobile Computing, Applications and Services (MobiCASE 2014, http://mobicase.org/2014/show/home)

You can find the complete list of proceedings here: http://proceedings.dev.icstweb.eu/2014/mobicase2014/file-storage/index.html#submissions

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