Design of Technology and Technology of Design. Activity Analysis as a Resource for a Personalised Approach for Patients with Parkinson Disease (original) (raw)
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Parkinsons disease patients perspective on context aware wearable technology for auditive assistance
2009
In this paper we present a wearable assistive technology for the freezing of gait (FOG) symptom in patients with Parkinson's disease (PO), with emphasis on subjective user appreciation. Patients with advanced PO often suffer from FOG, which is a sudden and transient inability to move. It often causes falls, interferes with daily activities and significantly impairs quality of life. Because gait deficits in PO patients are often resistant to pharmacologic treatment, effective nonpharmacologic treatments are of special interest. We have developed an ambulatory device that detects FOG episodes in real-time and provides an automatic cueing sound until the subject resumes walking. We tested our device on ten PO patients. Eight patients experienced FOG during our study. Over 8h of data has been recorded and 237 FOG events have been identified by professional physiotherapists in a post-hoc video analysis. The device detected the FOG events with a sensitivity of 73.1% and a specificity of 81.6% on a 0.5 sec frame based evaluation. The most important finding of the study is that we can provide online assistive feedback for FOG events in PO patients. Based on subjective reports, the majority of patients indicated that the context aware automatic cueing is beneficial for them.
Design and development of a gait training system for Parkinson’s disease
PLOS ONE
Rhythmic Auditory Stimulation (RAS) is an effective technique to improve gait and reduce freezing episodes for Persons with Parkinson's Disease (PwPD). The BeatHealth system, which comprises a mobile application, gait sensors, and a website, exploits the potential of the RAS technique. This paper describes the tools used for co-designing and evaluating the system and discusses the results and conclusions.
Technological support for people with Parkinson’s disease: a narrative review
Journal of gerontology and geriatrics, 2022
Background. Parkinson's disease resulting from the degeneration of specific areas of the brain, can cause severely disabling symptoms, such as freezing of gait. Freezing of gait increases the risk of falls through worsening of physical mobility, muscle stiffening, slow uncoordinated movements and often leads to hospitalization with significant worsening of quality of life for such patients. Indeed, older patients are at a significantly higher risk of negative outcomes related to PD. Objective. This work focuses on the most recent findings regarding non-invasive intervention and monitoring strategies to counteract the effects of freezing of gait. In addition, several devices can also provide support for diagnosis, treatment, and quality of daily life, especially in older patients with PD. Methods. This narrative review describes the current state of the art of devices based on cueing, monitoring and rehabilitation systems. Fifty-seven studies were selected. Results. Overall findings demonstrates that these smart devices can act as a valid aid tools able to: i) learn patient motor habits in order to intervene during a freezing of gait episode, ii) monitor daily conditions, iii) send and store data on disease progression, iv) provide useful information for rehabilitation programs in a clinical or home care environment. Conclusions. These technologies hold excellent prospects for patient treatment tailoring, especially in older patients in home care.
Proceedings of the 4th Augmented Human International Conference on - AH '13, 2013
Many people with Parkinson's disease suffer from freezing of gait, a debilitating temporary inability to pursue walking. Rehabilitation with wearable technology is promising. State of the art approaches face difficulties in providing the needed bio-feedback with a sufficient low-latency and high accuracy, as they rely solely on the crude analysis of movement patterns allowed by commercial motion sensors. Yet the medical literature hints at more sophisticated approaches. In this work we present our first step to address this with a rich multimodal approach combining physical and physiological sensors. We present the experimental recordings including 35 motion and 3 physiological sensors we conducted on 18 patients, collecting 23 hours of data. We provide best practices to ensure a robust data collection that considers real requirements for real world patients. To this end we show evidence from a user questionnaire that the system is low-invasive and that a multimodal view can leverage cross modal correlations for detection or even prediction of gait freeze episodes.
BioMed Research International, 2016
Self-report underpins our understanding of falls among people with Parkinson’s (PwP) as they largely happen unwitnessed at home. In this qualitative study, we used an ethnographic approach to investigatewhichin-home sensors, inwhichlocations, could gather useful data about fall risk. Over six weeks, we observed five independently mobile PwP at high risk of falling, at home. We made field notes about falls (prior events and concerns) and recorded movement with video, Kinect, and wearable sensors. The three women and two men (aged 71 to 79 years) having moderate or severe Parkinson’s were dependent on others and highly sedentary. We most commonly noted balance protection, loss, and restoration during chair transfers, walks across open spaces and through gaps, turns, steps up and down, and tasks in standing (all evident walking between chair and stairs, e.g.). Our unobtrusive sensors were acceptable to participants: they could detect instability during everyday activity at home and pot...
Entertainment Computing, 2014
The research presented in this paper proposes a set of design guidelines in the context of a Parkinson's Disease (PD) rehabilitation design framework for the development of serious games for the physical therapy of people with PD. The game design guidelines provided in the paper are informed by the study of the literature review and lessons learned from the pilot testing of serious games designed to suit the requirements of rehabilitation of patients with Parkinson's Disease. The proposed PD rehabilitation design framework employed for the games pilot testing utilises a low-cost, customized and off-the-shelf motion capture system (employing commercial game controllers) developed to cater for the unique requirement of the physical therapy of people with PD. Although design guidelines have been proposed before for the design of serious games in health, this is the first research paper to present guidelines for the design of serious games specifically for PD motor rehabilitation.
International Journal of Interactive Multimedia and Artificial Intelligence, 2019
Parkinson's Disease (PD) is the most common degenerative disorder after Alzheimer's disease. Generally affecting elderly groups, it has a strong limiting effect on physical functioning and performance of roles, vitality and general perception of health. Since the disease is progressive, the patient knows he's going to get worse. The deterioration is significant not only in mobility but also in pain, social isolation, and emotional reactions. Freezing is a phenomenon associated with this disease and it is characterized by a motor disorder that leaves the patient literally stuck to the ground. Mobeeze is designed with the main objective of providing health personnel with a tool to analyse, evaluate and monitor the progress of patients' disorders as well as the personalization and adaptation of rehabilitation sessions in patients with Parkinson's disease. Based on the characteristics measured in real time which will allow the strengthening effects of rehabilitation and help to assimilate them in the long term. The creation of Mobeeze allows the constitution of a system of analysis and evaluation of march disorders in real time, through natural interaction, virtual reality and artificial intelligence. In this project, we will analyse if these non-invasive technologies reduce the stress induced to the patient when he is feeling evaluated.
A real-world study of wearable sensors in Parkinson’s disease
npj Parkinson's Disease, 2021
Most wearable sensor studies in Parkinson’s disease have been conducted in the clinic and thus may not be a true representation of everyday symptoms and symptom variation. Our goal was to measure activity, gait, and tremor using wearable sensors inside and outside the clinic. In this observational study, we assessed motor features using wearable sensors developed by MC10, Inc. Participants wore five sensors, one on each limb and on the trunk, during an in-person clinic visit and for two days thereafter. Using the accelerometer data from the sensors, activity states (lying, sitting, standing, walking) were determined and steps per day were also computed by aggregating over 2 s walking intervals. For non-walking periods, tremor durations were identified that had a characteristic frequency between 3 and 10 Hz. We analyzed data from 17 individuals with Parkinson’s disease and 17 age-matched controls over an average 45.4 h of sensor wear. Individuals with Parkinson’s walked significantly...
2020
BACKGROUND Measuring free-living gait using wearable devices may offer higher granularity and temporal resolution than the current clinical assessments for patients with Parkinson disease (PD). However, increasing the number of devices worn on the body adds to the patient burden and impacts the compliance. OBJECTIVE This study aimed to investigate the impact of reducing the number of wearable devices on the ability to assess gait impairments in patients with PD. METHODS A total of 35 volunteers with PD and 60 healthy volunteers performed a gait task during 2 clinic visits. Participants with PD were assessed in the On and Off medication state using the Movement Disorder Society version of the Unified Parkinson Disease Rating Scale (MDS-UPDRS). Gait features derived from a single lumbar-mounted accelerometer were compared with those derived using 3 and 6 wearable devices for both participants with PD and healthy participants. RESULTS A comparable performance was observed for predictin...