DESIGN
European Technology for Business Ltd, Codicote, UK
A. Bertsch
Microsystems Laboratory, Ecole Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
Products under trial
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The Healthy Aims project (www.healthyaims.org) has set itself some clearly identified goals. In 2007, the final year of the four-year project, some of its products are entering clinical trials and others will be thoroughly tested under laboratory conditions ready for future clinical trials. Pilot human clinical trials have already started for a glaucoma sensor and a human activity monitor, and some preliminary results are reported in this article. By the end of 2007, pilot clinical trials will have been completed for a six degrees of freedom inertial measurement system, termed the “Out of the Gait Lab” system, and a catheter for urodynamics. Pilot animal trials will have been concluded for an intracranial pressure sensor system. In addition, a prototype planar three axis gyro will be available from HSG-IMIT (Villingen-Schwenningen, Germany, www.hsg-imit.de) for future integration into the hardware of the Out of the Gait Lab system.
These achievements have only been possible by including in the Consortium clinical experts/surgeons and manufacturers with experience in obtaining approvals for clinical trials for medical products. The sensor systems are all designed for medical diagnostics and integrate micro and nano technologies, wireless communications and biomaterials. The systems’ specifications defined the user requirements in terms of functionality, location, physical constraints and other factors such as the physical and mental abilities of the user. From this, a detailed design specification defined how each product would be realised and which technologies would need to be integrated. When each of the product partners had defined their requirements, the technology partners were able to ascertain what specific developments were required. Details of two of the sensor systems are provided in this article.
The glaucoma sensor system
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Figure 1: Thinning of the ASIC for the glaucoma sensor.
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The glaucoma sensor system comprises a contact lens, a pair of spectacles, a body worn unit and a PC for data processing and displaying results. The contact lens, made from medical grade silicone is the most complex component of the system because it needs to be compliant enough to take the form of an eyeball and measure the change in curvature that results from a change in internal pressure. It also needs to be able to read this pressure and communicate this information wirelessly, without the use of a battery. This has been achieved by integrating a strain gauge and antenna into the contact lens, together with an application specific integrated circuit (ASIC). The ASIC provides the circuitry for powering the wireless communication from the contact lens to a pair of spectacles and measuring the strain in the gauge.
Because the lens needs to be extremely compliant, a considerable amount of effort has been put into developing methods for thinning the ASIC and flip chip techniques to mount the ASIC onto the flexible membrane. ASIC thinning to 50 µm and subsequent flip chip bonding have been successfully demonstrated by EPFL (Microsystems Laboratory, Lausanne, Switzerland, www.epfl.ch) which is exploiting the system through a newly formed company, Sensimed (www.sensimed.ch). The technology is employed in its prototype glaucoma sensor (Figure 1). The assembled lens has been shown to provide adequate compliance for this application.
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Figure 2 : Complete glaucoma sensor system and lens mounted on the eye.
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The glaucoma sensor has already completed initial clinical trials with a wired unit. The first wireless contact lens version has now been produced. Pilot clinical trials have just started using the wireless system shown in Figure 2 and will be completed in 2007.
Activity monitor
Another sensor system being developed within the project is an activity monitor that incorporates a three-axis accelerometer with embedded algorithms. This is undergoing clinical trials. In 2007, this product will be extended to provide joint angle information and gait data similar to that which can be gathered in a Gait Lab.
The term “physical activity” can describe a range of tasks performed by human beings. Activity monitoring can use measurement technology to determine specific parameters relating to the physical activity being performed; these include the speed of walking, type of activity and the metabolic energy consumption during running.
There is now a growing body of research that has linked physical inactivity to many common diseases such as cardiovascular disease1 hypertension,2 diabetes mellitus3 and obesity.4 Although these studies typically rely on self reported activity levels, there is evidence to suggest that this is not a reliable means for accurately quantifying physical activity patterns.5,6 Thus, in many cases, it is preferable to use an automated activity monitoring system that provides direct measurement of the type, intensity and duration of activity across an extended period.
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Figure 3 : Healthy Aims activity monitor and the monitor mounted on the waist.
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The activity monitor shown in Figure 3 is worn on the waist, where it causes minimal impact to the user. It can run for up to 24 hours, storing data directly onto a card, and at the end of the activity the data is downloaded to the PC for analysis. Trials have been conducted on 10 healthy and 10 obese subjects under controlled clinical conditions at the Centre for Rehabilitation and Human Performance Research, University of Salford (USAL) (Salford, UK, www.ihscr.salford.ac.uk/CRHPR/ The trial protocol included a period on the treadmill followed by a range of outdoor activities including hopping, stair climbing, walking, jogging and running.
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Figure 4 : Activity plotted against VO2/mass for a healthy subject on a treadmill.
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Figure 4 shows the output from the activity monitor for one of the healthy subjects on a treadmill. This has been plotted against the volumetric rate of oxygen uptake divided by the body mass (VO2/mass), which is a measure that is representative of expended energy. From this graph it can be seen that the activity, which was determined using proprietary algorithms, correlates well with VO2/mass at three different walking speeds. Thus the system can be used to determine energy expenditure during gait related activities.
European Technology For Business (www.etb.co.uk) is now working on developing the system to derive other stride characteristics. These are important parameters for a variety of applications, including athletes in training,7 the elderly and people suffering from medical conditions such as Parkinson’s disease and dementia. Accurate monitoring of these parameters over time and the development of feedback mechanisms can help these groups to improve their gait. USAL is also developing specific algorithms to identify precise activities using the activity monitor, for example, stair climbing, walking, running, sitting and lying. By the end of 2007 these algorithms will have been demonstrated.
Vital clinical input
At the end of the Healthy Aims project a range of new medical diagnostic systems using micro and nano-technology will be available, including the sensor systems described in this article. These considerable achievements are attributable to the fact that clinical teams have led the product partners and have defined their exact requirements. The final year of the project is providing exciting results for the clinical experts and will lead the way for future exploitation of the medical diagnostic equipment. It should also provide valuable data for future applied research in this field.
Acknowledgment
This research project is funded by the European Information Societies Technology Programme (IST-2002-1-001837).
References
1. N.C. Barengo et al., “Low Physical Activity as a Predictor for Total and Cardiovascular Disease Mortality in Middle-Aged Men and Women in Finland,” Eur. Heart J., 25, 24, 2204–2211 (2004.)
2. S.N Blair et al., “Health Promotion for Educators: Impact on Health Behaviors, Satisfaction and General Well-Being,” Am. J. Public Health, 74, 2, 147–149 (1984).
3. J.E. Manson et al., “Physical Activity and Incidence of Non-Insulin-Dependent Diabetes Mellitus in Women,” Lancet, 338, 8770, 774–778 (1991).
4. P. Kokkinos and G. Moutsatsos, “Obesity and Cardiovascular Disease: The Role of Diet and Physical Activity, ” J. Cardiopulm. Rehabil., 24, 3, 197–203 (200.).
5. K.R. Fox and C. Riddoch, “Charting the Physical Activity Patterns of Contemporary Children and Adolescents,” Proceedings of the Nutrition Society, 59, 4, 497–504 (2000).
6. S.M. Patterson et al., “Automated Physical Activity Monitoring – Validation and Comparison with Physiological and Self-Report Measures,” Psychophysiology, 30, 3, 296–305 (1993).
7. L. Brown and L. Ferringo, “Training for Speed, Agility and Quickness,” 2nd Edition, Human Kinetics, Champaign, Illinois USA, ISBN: 0736058737.
Dr. Diana Hodgins* MBE DSc (Honorary) is Project Co-ordinator of Healthy Aims and Managing Director of European Technology for Business Ltd, Codicote Innovation Centre, St. Albans Road, Codicote, SG4 8WH, UK, tel. +44 1438 822 822, e-mail: diana.hodgins@etb.co.uk, www.etb.co.uk
Dr. Arnaud Bertsch is Senior Scientist at Ecole Polytechnique Fédérale de Lausanne, Microsystems Laboratory STI-LMIS4 BM 3.124, Station 17, Lausanne CH-1015, Switzerland.
* To whom all correspondence should be directed.
