Innovative Medical Engineering for an Aging Society

In this contribution we give an overview of the activities of the AgeTech group
within the department of micro and medical device technology of the TU München.
Specific Human-Machine Interaction aspects will be addressed in the second
part of the contribution. We present applications and solutions in the
fields of wearable movement analysis in everyday, emergency and diagnostic/therapeutic
scenarios. Future challenges in the development of HumanMachine
Interaction are addressed in the conclusion.

In everyday life, even half an hour of active movement would have a great impact on the
health of the cardiovascular system. However, most people don’t have the ability to realize or
remember correctly how much they really move actively. For this purpose, the Motionlogger
is a system which can be carried in a pocket and which is able to determine whether the wearer
is in a state of rest, walking or running. This detection is done online and without the necessity
of training. Every state is stored on the system with its duration and starting time
thanks to a real time clock embedded into the system. At home, the user can then read out the
recorded data connecting the Motionlogger to a docking station. It gives the user a feedback
about how much time he moved actively in the course of the day and whether he should
change his behavior [1].

The motion pullover has been developed in order to detect falls, which are a great problem
and reason of apprehension in elderly people living alone. It consists of a pullover with embedded
acceleration sensors and software running on an embedded microcontroller capable of
detecting falls of the wearer. The pullover is fully washable after removing the battery pack
using push-buttons [2]. The same pullover, together with an evaluation software running on a
tablet PC, can be used to perform an automated Parkinson’s Disease (PD) rating according to
the Unified Parkinson Disease Rating Scale (UPDRS). For this purpose, the software determines
the UPDRS rating starting from tremor characteristics like frequency or amplitude
measured while the wearer performs specific exercises defined in the UPDRS. The software
also guides the wearer through the test procedure, showing which pose he has to strike and for
how long he has to hold it. This way, the pullover offers a tool to perform an objective UPDRS
rating also at home and with a higher frequency compared to ratings done only when the
patient visits the clinic [3].

Freezing of Gait (FOG) is also a problem associated with PD, in which patients report a sensation of “having their feet glued to the ground”. This is frustrating in everyday life and can even lead to falls. A system able to measure the frequency of episodes in everyday life would help to determine the efficiency of therapy. An online detection can also be used to give the patients a cue when FOGs occur in order to help them to end the episode. The motion pantshave been developed for this purpose. Similar to the motion pullover, they have acceleration sensors and electronics embedded in order to record movements of the wearer. After wearing
the textile, an offline analysis on a PC is able to detect when FOG episodes occurred [4]. In
future, the goal is to perform the analysis online and on the microcontroller embedded in the
textile.

As a support tool in Deep Brain Stimulation (DBS) surgery, we are developing a sensory
glove which can be used to determine tremor ratings intraoperatively. The glove must enable
the surgeon to quickly get an objective measure of finger tapping amplitude and speed, tremor
frequency and amplitude as well as arm rigor strength for each electrode position and stimulation
current in order to determine the optimal position and current value among the tested
ones [5].

The Human-Machine Interaction hardware and software employed to develop the described
devices is of various nature: it is advisable to employ different solutions depending on whether
the scope is to convey basic information or to interact with a home desktop solution, in the
living room, in a clinical setting or in transit.

For basic information, usually components like Light Emitting Diodes (LEDs), buzzers, buttons
or Liquid Crystal Displays (LCDs) are used. Those are simple and fast to understand for
the user and cheap in development. However, they convey only a small amount of information
and can thus lead to misunderstandings of the concept of operations if not clearly designed
or labeled.

The desktop environment offers space, volume, current and internet connectivity. Thus, PCbased systems with bigger screens even with touch functionality can be used. They enable more complex information display and input, but come with higher costs and a higher user
inhibition. Thus, especially during the development of the Home Care Unit [6], it became
clear that user involvement in early development stages is crucial for reaching a product the
user accepts.

In the living room environment, especially for elderly people, the TV is an underestimated user interface. It has a high visibility as it is often switched on and watched, it is a familiar device which the user knows and thus has low inhibition to use, and is able to display fairly complex information and messages or to overlay it to live TV broadcast. In a clinical environment, where high computational power and complex user interaction is needed, tablet computers based on a wheeled platform are a good choice. They render the device mobile and easy to carry from room to room or to store away. For mobile use in everyday life, touch-based smartphones are becoming a platform of choice: They are flexible, continuously usable and thus time saving and offer cheap and fast methods to spread software when the potential user already owns the device. However, they are subject to rapid changes, have limited computational power and might get slow or crash depending on the number of installed or running applications. Thus, their use should be limited to user interaction or storage of non-time-critical data and running non-safety-relevant processes. For the latter, dedicated devices which are fully controllable by the medical device developing company should be used [7].

Many challenges and developments are ahead in the future regarding Human-Machine Interaction
in medical engineering systems for an aging society: How can we objectively measure
the ease-of-use of a device, and how do we define how high the ease-of use should be for a
healthy person with a specific “cognitive age”? How does this change when the person has a
specific condition? Devices will have to meet the right assistance level in order to keep the
user’s abilities high by slightly challenging them. However, they will have to be able to learn
about the user’s cognitive decline and adapt to it. Finally, people will have to be convinced
earlier in their life to do more for their personal healthcare.

References:
[1] Czabke, A.; Marsch, S.; Lueth, T.C. (2011): “Accelerometer based real-time activity analysis
on a microcontroller”, Pervasive Computing Technologies for Healthcare (PervasiveHealth),
2011 5th International Conference on, pp. 40 – 46.

[2] Niazmand, K.; Jehle, C.; D'Angelo, L.T.; Lueth, T.C. (2010): “A new washable low-cost
garment for everyday fall detection”, Engineering in Medicine and Biology Society (EMBC),
2010 Annual International Conference of the IEEE, pp. 6377 – 6380.

[3] Niazmand, K.; Tonn, K.; Kalaras, A.; Kammermeier, S.; Boetzel, K.; Mehrkens, J. H.;
Lueth, T. C. (2011): “A measurement device for motion analysis of patients with Parkinson's
disease using sensor based smart clothes”, Pervasive Computing Technologies for Healthcare
(PervasiveHealth), 2011 5th International Conference on, pp. 9 – 16.

[4] Niazmand, K.; Tonn, K.; Zhao, Y.; Fietzek, U.M.; Schroeteler, F.; Ziegler, K.; CeballosBaumann,
A.-O.; Lueth, T.C. (2011): “Freezing of Gait detection in Parkinson‟s disease using
accelerometer based smart clothes”, IEEE BioCAS Conference 2011, in print.

[5] Niazmand, K.; Tonn, K.; Kalaras, A.; Fietzek, U.M.; Mehrkens, J.H.; Lueth, T.C. (2011):
“Quantitative evaluation of Parkinson's disease using sensor based smart glove”, ComputerBased
Medical Systems (CBMS), 2011 24th International Symposium on, pp. 1 – 8.

[6] Czabke, A.; Loeschke, J.; Lueth, T.C. (2011): “Concept and modular telemedicine platform
for measuring of vital signs, ADL and behavioral patterns of elderly in home settings”,
Engineering in Medicine and Biology Society (EMBC), 2011 Annual International Conference
of the IEEE, pp. 3164 – 3167.

[7] D’Angelo, L.T.; Schneider, M.; Neugebauer, P.; Lueth, T.C. (2011): “A Sensor Network
to iPhone Interface separating Continuous and Sporadic Processes in Mobile Telemedicine”,
Engineering in Medicine and Biology Society (EMBC), 2011 Annual International Conference
of the IEEE, pp. 1528 – 1531.

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