In an everyday environment, the state of health or even age of a person is recognisable by how they walk or their “gait.” For example, people generally walk more slowly as they get older and may start to shuffle. Those that have suffered a stroke may drag one of their legs and people with an injured knee or hip joint will walk with a definite asymmetry.
The clinical research world recognised the importance of “gait” information many years ago and today there are numerous groups worldwide writing articles and organising conferences to discuss issues that relate gait profiles to medical conditions. However, the benefit of these research and development (R&D) activities is not being received by clinical practitioners or the end user. In current medical diagnosis, gait information is rarely used as an input. This is because even though a human can intuitively watch a person walk and perform a diagnosis on his/her gait pattern, actually quantifying a person’s gait is difficult.
This article describes the different medical applications that would benefit from a reliable and quantifiable method of monitoring a person’s gait. It also describes the different technologies that are, or could be, applied to those applications. For the purpose of this article, gait analysis covers stride length, stride rate (rhythm) and speed, and joint angle. Example applications of where a gait analyser could be employed are outlined below.
Stride frequency affects the contact time and pressure–time impulse in Type 2 diabetes patients and is related to plantar ulceration.1 A system that can determine the preferred stride frequency (SF) for a patient and then provide a revised, faster SF for them to walk by would help reduce the occurrence of plantar ulcers and result in an improved quality of life for the person and reduced health care cost. Typical changes in SF need to be approximately 5% and to be monitored throughout the day. Ideally, the rhythm (cadence) would be given to the patients in real time so that they can monitor their own walking pattern and compare it with their target.
Gait function is affected by osteo-arthritis in the knee and can be improved by training.2 As part of a clinical assessment, the normal gait and particularly stride length should be determined by a monitoring system. The person could then be put onto an educational programme and improvements in their gait function could be objectively measured on a regular basis. When surgery is required such as total knee arthroplasty, a measurement of knee angle pre- and post-operation is an excellent method of monitoring the rehabilitation progress.
Gait analysis before lower extremity orthopaedic surgery could reduce the rate of additional surgery by allowing more appropriate surgery to be done initially. Gait monitoring post surgery would also inform the clinician if the patient is over or under exercising, both of which can have a detrimental effect on the rehabilitation process.
Fall detection in elderly people
Elderly people’s stride rate tends to increase and their stride length decreases, which results in a higher risk of falling.3 A gait monitoring system could be used as part of an assessment protocol to ascertain which elderly people are more at risk of falling. Gait training exercises could then be provided and the effect on their stride rate could be measured accurately to determine any improvements. Stumbling is also an indication that a person is more at risk of falling and this could be monitored directly or ascertained from a change in the gait profile over time.
The relationship between stride length and stride frequency exhibits abnormalities in people with Parkinson’s disease4 and there are a number of different forms of treatment now being considered. In some cases, Levodopa medication can steady a person’s gait by improving their stride length. The benefit of this treatment could be quantified by monitoring the stride characteristic before and after treatment. Another possible treatment is using functional electrical stimulation (FES).5 In this case, the FES helps maintain a stride rate and stride length and results in a more stable gait whereby the patient is less likely to fall.
Gait abnormalities are an early clinical symptom in normal pressure hydrocephalus and gait change is often used when deciding whether or not to perform shunt surgery.6 A gait analyser system enables the clinician to test the gait of a hydrocephalus patient before and after shunt surgery. It could also be considered as a means of monitoring whether the shunt has blocked.
These are some examples of where a gait analyser could be applied in the medical sector. However, at this time only research activities to prove the validity of the statements have been performed. This is deemed to be because of the lack of commercially available systems that can be used for large scale monitoring.
Optical gait analysis systems
Currently, the most well known and most frequently used type of analysis is an optical system employed in a gait laboratory. These systems are used to monitor the movement of a person whilst they walk around a room (Figure 1). Optical data can be analysed in a number of ways depending on the precise information and the level of accuracy that are required, and the time available. In the simplest form the optical data are slowed down and a visual interpretation provided. In the more complex research applications, all of the gait variables listed previously (stride length, rate and speed; and joint angle) are determined. The University of Salford (USAL), Greater Manchester, UK, is one example of a leading gait laboratory, which publishes numerous academic papers for different clinical applications, for example, stroke patients.7 The laboratory uses a variety of optical systems, including Vicon (www.vicon.com) and Qualisys (www.qualisys.com), two of the main suppliers to gait laboratories.
There are limitations with any form of optical system. The major one is that it takes a considerable amount of time to obtain quantifiable gait information from the optical images and this results in a high cost for a gait laboratory test. Other limitations are that the field of view is restricted to approximately one to two strides and the person being analysed may behave differently when they know they are being monitored, which is known as “white coat syndrome.”
Because optical systems are fixed systems, they cannot be used for any of the medical applications in which home monitoring is required. Gait laboratories can only be used for medical assessments of gait characteristics in the laboratory and even then it is difficult to gather sufficient data for values to be statistically significant.
An adaptation of the gait laboratory is to put the person on a treadmill and monitor their strides over a period of time at set speeds. This unfortunately brings in another variable, which is how a person walks on a treadmill compared with in their natural environment. It is also a fixed system and therefore does not overcome the major limitation, that is, the inability to monitor a person over time during their normal daily activities.
Motion sensing systems
During the past ten years, micro- and nanotechnology have developed rapidly and now there is a range of on-the-body sensors available that can be used to monitor human body motion. The first were accelerometer-based and generally wired to a PC and these have become Bluetooth enabled or standalone storage systems.8,9 More recently, full inertial measuring units (IMU), which provide data on all six degrees of freedom, have become available, integrating solid state gyroscopes with accelerometers.10,11
Researchers have used these sensor modules to analyse body movement in a range of different medical applications. Papers have been written on how to use accelerometers in a variety of ways, for example, to determine how many strides a person has taken (step count) over a set period, his/her activity level, the type of activity and the posture of a person. One example of this type of system is the StepWatch from Ortho-Care (www.orthocare.com).
Some of the most advanced groups involved in gait analysis in Europe are based in The Netherlands and a number of these R&D groups are analysing gait for a wide variety of medical conditions. These include the University of Maastricht12 (www.unimaas.nl) and Roessingh Research and Development (www.rrd.nl).13 There are also two companies supplying hardware and software used in clinical research. XSens (www.xsens.com) provides a full IMU that can be tethered or Bluetooth-enabled that sends data to a PC. These units have been used for a range of research projects into biomechanics, in which the user generally makes his/her own analysis of the data. The second company, McRoberts (www.mcroberts.nl) has developed storage accelerometer and gyro-based systems for different human motion applications. Researchers can use these to develop their own algorithms, or clinical teams can use the company’s software solutions for specific measurements.
However, until recently there were no commercial systems that could provide all the gait information described in this article. A new system has been developed with European Union funding.
New system approach for gait analysis
Figure 2: Storage accelerometer and GPS to determine stride characteristics.
Four years ago, the European Union Framework 6 Programme (FP6) Healthy Aims project was launched. This project, which is co-ordinated by European Technology for Business (ETB), set clear goals to develop a range of medical implants and diagnostic equipment. One aspect of the project was to develop diagnostic equipment for human gait analysis after recognising the need for “out of the gait lab” systems. ETB worked closely with USAL to define the system requirements before starting on the hardware and software development. In 2005, the first wired accelerometer unit was produced and tested. This concept was further developed in 2006 to produce a storage three-axis accelerometer module, which enabled a person to be monitored anywhere. The company also began developing its own signal processing algorithms, recognising the need for an accurate system to address the specific medical applications. With only a three-axis accelerometer it is impossible to accurately determine stride length, stride rate and speed, simply because rotational effects cannot be taken into consideration.
Figure 3: New IMU for monitoring joint angle.
The system solution therefore used the accelerometer module to determine stride rate and a global positioning system unit was used to determine speed, thus enabling stride length to be calculated using the simple formula: speed/stride rate = stride length.
This system, shown in Figure 2, underwent clinical trials in 2007 and demonstrated one per cent accuracy at normal walking speeds, thus making it suitable for use in the medical applications previously defined. Since then, application trials have started in the cardiology and orthopaedic sectors with success.
The accelerometer module was not suitable for monitoring joint angle and therefore the sensor unit was modified to include three axes of rotation, thus resulting in a full IMU, see Figure 3. The size and mass of this module was kept the same by using innovative packaging techniques to ensure that it was also light and small enough not to affect limb movement. The same storage capability was maintained and two additional critical features were added: synchronisation between units and less than 10 ms drift in one hour. Collectively, these enable two or more units to be placed on different body segments and their position relative to each other can be monitored, thus providing joint angle data. This module was available in late 2007 and underwent clinical trials in the USAL gait laboratory. Results indicate that it is as accurate as optical systems at walking and jogging speeds. The system is starting trials in a variety of medical applications.
Clinical research groups around the world have identified clear links between human gait characteristics and different medical conditions. Until recently, however, the commercial sector has been unable to offer sensor systems that are able to accurately monitor these parameters in a normal environment. This has meant that clinical practitioners are unable to use these monitoring methods to help them in their diagnosis and rehabilitation of patients. This gap was identified by the team in the FP6 Healthy Aims project and equipment has been produced that is designed for assessing gait characteristics for people with a range of medical conditions. It is anticipated that during the next few years this trend will continue and other commercial organisations will introduce sensor systems to address these markets. This will then enable clinical practitioners to use gait assessment as part of a diagnosis and monitoring after treatment programme, something that the clinicians have been requesting.
1. A. Bellmeyer and J. Strasser, “Effect of Stride Frequency on Plantar Loading in Type 2 Diabetes,” http://murphylibrary.uwlax.edu/digital/jur/2001/bellmeyer-strasser.pdf
2. M.G.E. Peterson et al., “Effect of a Walking Program on Gait Characteristics In Patients With Osteoarthritis,” Arthritis Care and Research, 6,1, 11–16 (1993).
3. Y. Barak et al., “Gait Characteristics of Elderly People With a History of Falls: A Dynamic Approach,” Physical Therapy, 86, 11, 1501–1510 (2006).
4. M. Morris et al., “Abnormalities in the Stride Length-Cadence Relation in Parkinsonian Gait,” Movement Disorders, 13, 1, 61–69 (1998).
5. S. Finn et al., “Using Functional Electrical Stimulation in Parkinson’s Disease,” www.salisburyfes.com/dropfoot.htm
6. M. Williams et al., “Objective Assessment of Gait in Normal-Pressure Hydrocephalus,” American Journal of Physical Medicine and Rehabilitation, 87, 1, 39–45 (2008).
7. S. Thies et al., “A Practical Assessment Tool for Repeatability of Upper Limb Movement in Stroke,” in Proc. Royal Academy of Engineering Futures Meeting, Durham, UK, 2006. USAL: www.salford.ac.uk
8. D. Hodgins, “Human Motion Analysis,” Medical Device Technology, 17,1, 12–15 (2006).
9. M Brandes et al “Accelerometry Based Assessment of Gait Parameters in Children,” Gait and Posture, 24, 4, 482–486 (2006).
10. S. Beauregard “Omnidirectional Pedestrian Navigation for First Responders,” Proc. of Fourth Workshop on Positioning, Navigation and Communication, Hannover, Germany, March 2007.
11. A. Gallagher et al., “An Efficient Real-Time Human Positive Tracking Algorithm Using Low-Cost Inertial and Magnetic Sensors,” Intelligent Robots and Systems 2004 Conference, 28 September to 2 October 2004, Sendai, Japan.
12. F.P.H. Hamers et al., “Cat Walk-Assisted Gait Analysis in the Assessment of Spinal Cord Injury,” Journal of Neurotrauma, 23, 3–4, 537–548 (2006).
13. T. Yoshida et al., “Development of a Wearable Surveillance System Using Gait Analysis,” Telemedicine and E-Health, 13, 6, 703–713 (2007).
Dr Diana Hodgins, MBE DSc (Honorary) is Managing Director of European Technology for Business Ltd, Codicote Innovation Centre, St Albans Road, Codicote, UK tel. +44 1438 822 822, e-mail: firstname.lastname@example.org, www.etb.co.uk