, Dr. Heiko Gaßner,
11/2018 – 04/2019
Changes in gait patterns are among the most prominent symptoms in many neurological diseases,
such as Parkinson’s disease (PD). Gait analysis supports physicians in the ongoing diagnosis,
treatment and wider understanding of the disease. Recording gait data and extracting gait
parameters allow classification of patients’ disease stages  or the forecast of risk of falling .
Gait analysis can be divided into two major aspects: Micro analysis and macro analysis. Micro
analysis refers to spatio-temporal gait analysis focusing on step times, step lengths and fluctuations
that have been shown to be sensitive in ageing and pathological studies [3, 4]. On the other side,
macro analysis describes the broader signal profile representing the general walking activity.
Combining both approaches in continuous long-term monitoring offers more informative data and
improves gait-related outcomes . To adapt this approach in the individual patient’s everyday life,
the implementation of a simple, cheap and unobtrusive technical system is needed. A widely used
system to fulfill those requirements is the IMU (inertial measurement unit) system, which allows to
be carried by patients over several days and does not require laboratory settings . The macro
signal analysis is based on the investigation of walking bouts. First, a suitable definition of a walking
bout needs to be scrutinized. For example, a well-defined number of steps that fulfill a certain kind
of relationship with each other are summed up to one bout and a phase of rest marks the end of one
walking bout . Important outcome parameters of macro gait analysis are (amongst others) the
distribution of walking bouts of different lengths and the variation of micro gait parameters within
The goal of this bachelor thesis is the investigation and development of a set of tools to process longterm
gait data collected in the home environment within the FallRiskPD study . Besides their
activities of daily living, the participating PD patients are instructed to perform specific clinical gait
tests in their home environment. From a technical perspective, the aim is to automatically detect
those gait tests in the continuous data. Furthermore, macro gait parameters such as distribution of
walking bout length in the time and step domain of patients are going to be investigated and
compared to those of healthy participants to find outcomes which are characteristic for the disease.
- Schlachetzki, JCM., et al. (2017). “Wearable sensors objectively measure gait parameters in
Parkinson’s disease.” PLoS ONE Vol. 12, No. 10: 2.
- Del Din, S., et al. (2017). “Analysis of Free-Living Gait in Older Adults with and without
Parkinson’s Disease and With and Without a History of Falls: Identifying Generic and
Disease-Specific Characteristics.” Journals of Gerontology: MEDICAL SCIENCES, 2018,
Vol. 00, No. 00: 1.
- Verghese, J., et al. (2007). “Quantitative gait dysfunction and risk of cognitive decline and
dementia.” J. Neurol. Neurosurg. Psychiatry Vol. 78 No. 9: 29-35.
- Lord, S., et al. (2013). “Independent domains of gait in older adults and associated motor and
nonmotor attributes: validation of a factor analysis approach.” J. Gerontol. A Vol. 68, No. 7:
- Hickey, A., et al. (2017). “Detecting free-living steps and walking bouts: validating an
algorithm for macro gait analysis” Physiological Measurement, 2017, Vol. 38, No. 01: 1.
- Orendurff, M., et al. (2008). “How humans walk: Bout duration, steps per bout, and rest
duration.” Journal of Rehabilitation Research & Development 2008, Vol. 45, No. 07: 1.