6 research outputs found
Objective assessment of upper limb motor symptoms in Parkinson's Disease using body-worn sensors
MD ThesisBackground
There is a need for an objective method of symptom assessment in Parkinson's disease (PD) to enable better treatment decisions and to aid evaluation of new treatments. Current assessment methods; patient-completed symptom diaries and clinical rating scales, have limitations. Accelerometers (sensors capable of capturing data on human movement) and analysis using artificial neural networks (ANNs) have shown potential as a method of motor symptom evaluation in PD. It is unknown whether symptom monitoring with body-worn sensors is acceptable to PD patients due to a lack of previous research.
Methods
34 participants with PD wore bilateral wrist-worn accelerometers for 4 hours in a research facility (phase 1) and then for 7 days in their homes (phase 2) whilst also completing symptom diaries. An ANN designed to predict a patient’s motor status, was developed and trained based on accelerometer data during phase 2. ANN performance was evaluated (leave-one-out approach) against patient-completed symptom diaries during phase 2, and against clinician rating of disease state during phase 1 observations. Participants’ views regarding the sensors were obtained via a Likert-style questionnaire completed after each phase. Differences in responses between phases were assessed for using the Wilcoxon rank-sum test.
Results
ANN-derived values of the proportion of time in each disease state (phase 2), showed strong, significant correlations with values derived from patient-completed symptom diaries. ANN disease state recognition during phase 1 was sub-optimal. High concordance with sensors was seen. Prolonged wearing of the sensors did not adversely affect participants’ opinions on the wearability of the sensors, when compared to their responses following phase 1
Conclusions
Accelerometers and ANNs produced results comparable to those of symptom diaries.
Our findings suggest that long-term monitoring with wrist-worn sensors is acceptable to PD patients
Objective evaluation of Parkinson's disease bradykinesia
Bradykinesia is the fundamental motor feature of Parkinson’s disease - obligatory for diagnosis and central to monitoring. It is a complex clinicalsign that describes movements with slow speed, small amplitude, irregular rhythm, brief pauses and progressive decrements. Clinical ascertainment of the presence and severity of bradykinesia relies on subjective interpretation of these components, with considerable variability amongst clinicians, and this may contribute to diagnostic error and inaccurate monitoring in Parkinson’s disease. The primary aim of this thesis was to assess whether a novel non-invasive device could objectively measure bradykinesia and predict diagnostic classification of movement data from Parkinson’s disease patients and healthy controls. The second aim was to evaluate how objective measures of bradykinesia correlate with clinical measures of bradykinesia severity. The third aim was to investigate the characteristic kinematic features of bradykinesia. Forty-nine patients with Parkinson’s disease and 41 healthy controls were recruited in Leeds. They performed a repetitive finger-tapping task for 30 seconds whilst wearing small electromagnetic tracking sensors on their finger and thumb. Movement data was analysed using two different methods - statistical measures of the separable components of bradykinesia and a computer science technique called evolutionary algorithms. Validation data collected independently from 13 patients and nine healthy controls in San Francisco was used to assess whether the results generalised. The evolutionary algorithm technique was slightly superior at classifying the movement data into the correct diagnostic groups, especially for the mildest clinical grades of bradykinesia, and they generalised to the independent group data. The objective measures of finger tapping correlated well with clinical grades of bradykinesia severity. Detailed analysis of the data suggests that a defining feature of Parkinson’s disease bradykinesia called the sequence effect may be a physiological rather than a pathological phenomenon. The results inform the development of a device that may support clinical diagnosis and monitoring of Parkinson’s disease and also be used to investigate bradykinesia
The design and evaluation of novel technologies for the self monitoring and management of Parkinson's symptoms
PhD ThesisThis thesis explores how digital technologies might better support people with Parkinson’s
(PwP) to take control of their condition, by engaging in self monitoring and management
practices. The specific focus of this thesis is around issues managed by Speech and Language
Therapists (SLTs) (namely drooling and speech and voice changes). Three case studies were
used to explore the ways that different technologies might be configured to aid the self
monitoring and management of these speech and drooling symptoms.
The first case study describes an evaluation of PDCue, a wrist worn device to assist
the self management of drooling through the use of a temporal cueing method, to increase
swallowing frequency. This study showed evidence that drooling can be behaviourally self
managed through cueing—like other symptoms of Parkinson’s such as gait freezing—and
proved a viable first step towards re-considering the use of additional medications as a first
option for drooling treatment. However, whilst this study proved successful in
understanding the ways in which a simple, temporal cueing technique might support
drooling management, it opened up questions around the ways in which PwP might use
technology to actively think about and understand their condition through self monitoring,
and use this information to support self management practices further. In response, the
second case study describes the design and evaluation of LApp, an application to support
both the self monitoring and management of vocal loudness issues through the use of an insitu
cueing approach. The Google Glass was chosen as the platform to run the cueing
method on, due to its technical capabilities as a multi-sensor, wearable platform, to analyse
a constant stream of audio and provide real time visual prompts to support the wearer in
increasing their volume at times when it is needed in conversation. This study highlighted
how participants saw value in LApp in supporting their loudness issues, but also noted a
desire for participants to understand more about their speech and the SLT strategies that
they were required to do in order to improve it. The third case study drew upon this desire
for increased understanding by developing and evaluating Speeching, which employed
crowdsourcing through a smartphone application to support the self monitoring of speech
and voice changes, through the provision of human feedback, and the subsequent effect
that this feedback had on self management practices. This study yielded positive responses
from participants, who valued the anonymous feedback from the crowd and the support
that this provided them in configuring their home based speech practice.
A final discussion chapter draws the 3 case studies together and discusses the
lessons learned throughout the research. It discusses the overall research questions for the
thesis in detail and describes the implications of the research for the wider HCI and medical
communities. A framework is presented which aims to visualise the levels of agency that the
studied technologies afforded and the levels of responsiveness required by participants to
make sense of, and implement the information being provided by the devices in order to
facilitate a change to the self monitoring and management practices. Through the design
and evaluation of the described technologies and a synthesis of the findings across the span
of the research, this thesis explores the ways in which PwP, with a diverse range of
symptoms and related physical, social and emotional issues, might value digital technologies
and their potential to facilitate new forms of self monitoring and self management in their
everyday lives.The National Institute of Health Research (NIHR):
The Engineering and Physical Sciences Research Council (EPSRC):
Gordon Chapman Memorial Fund
Development and Evaluation of AI-based Parkinson's Disease Related Motor Symptom Detection Algorithms
Parkinson's Disease (PD) is a chronic, progressive, neurodegenerative disorder that is typically characterized by a loss of (motor) function, increased slowness and rigidity. Due to a lack of feasible biomarkers, progression cannot easily be quantified with objective measures. For the same reason, neurologists have to revert to monitoring of (motor) symptoms (i.e. by means of subjective and often inaccurate patient diaries) in order to evaluate a medication's effectiveness. Replacing or supplementing these diaries with an automatic and objective assessment of symptoms and side effects could drastically reduce manual efforts and potentially help in personalizing and improving medication regime. In turn, appearance of symptoms could be reduced and the patient's quality of life increased. The objective of this thesis is two-fold: (1) development and improvement of algorithms for detecting PD related motor symptoms and (2) to develop a software framework for time series analysis