Harry J. Griffin

Quality of life in Parkinson's disease: The relative importance of the symptoms

A body of literature now exists, which demonstrates that idiopathic Parkinsons disease (PD) has a major negative impact on quality of life (QoL), and that depression and cognitive impairment are among the main predictors of poor QoL in this disorder. Relatively little work has been done to assess the differential contribution of the specific symptoms of PD to QoL, which was the aim of this study. One hundred thirty patients with PD completed a booklet of questionnaires, which included the PDQ39 as a disease‐specific measure of QoL, a symptom checklist, a mobility checklist, as well as patient ratings of disease stage and disability. The results indicated that the contribution of physical, medication‐related, and cognitive/psychiatric symptoms to QoL can be significant. Sudden unpredictable on/off states, difficulty in dressing, difficulty in walking, falls, depression, and confusion were PD symptoms, which significantly influenced QoL scores. Among the mobility problems associated with PD, start hesitation, shuffling gait, freezing, festination, propulsion, and difficulty in turning had a significant effect on QoL scores. In addition to depression and anxiety, the major predictors of QoL were shuffling, difficulty turning, falls, difficulty in dressing, fatigue, confusion, autonomic disturbance particularly urinary incontinence, unpredictable on/off fluctuations, and sensory symptoms such as pain. The implications of these results for the medical management of PD are discussed.

The factors that induce or overcome freezing of gait in Parkinson's disease

Freezing of gait (FoG), a transient halt in walking, is a major mobility problem for patients with Parkinson’s disease (PD). This study examined the factors that induce FoG, and identified the cues and strategies that help overcome it through a postal survey of 130 PD patients. 72% reported FoG. The factors that commonly induced FoG were turning, fatigue, confined spaces and stressful situations, in addition to emotional factors. FoG was also ameliorated by various attentional and external cueing strategies. The concept of paradoxical kinesis, the potential neural substrates of such external cueing effects, and their importance for rehabilitation in PD are discussed.

The effect of real and virtual visual cues on walking in Parkinson’s disease

Patients with Parkinson’s disease (PwPD) have a slow, shuffling gait, marked by sporadic freezing of gait (FoG) during which effective stepping ceases temporarily. As these gait problems are not commonly improved by medical and surgical treatments, alternative approaches to manage these problems have been adopted. The aim of this study was to evaluate the effect of real and virtual visual cues on walking in PD. We assessed 26 mid-stage PwPD, on and off medication, on a laboratory-based walking task which simulated real world challenges by incorporating FoG triggers and using appropriate placebo conditions. Cueing interventions were presented via virtual reality glasses (VRG rhythmic, visual flow and static placebo cues), and as transverse lines (TL) on the walkway. Objective measures of gait (task completion time; velocity, cadence, stride length; FoG frequency) and self-rated fear of falling (FoF) were recorded. Cueing intervention affected task completion time only off medication. Whereas placebo VRG cues provided no improvement in walking, visual flow VRG cues marginally reduced the task completion time. TL on the floor elicited more substantial improvements in gait with reduced cadence, increased stride length and reduced FoG frequency. VRG rhythmic cueing impaired overall walking. Notably, a final no-intervention condition yielded quicker task completion, greater walking velocity, increased stride length and less frequent FoG. Although the VRG produced modest improvements only in the visual flow condition, their flexibility is an advantage. These results endorse the use of TL and justify further testing and customisation of VRG cues for individual PwPD.

On the nature of fear of falling in Parkinson's disease.

In the elderly, fear of falling (FoF) can lead to activity restriction and affect quality of life (QoL). Our aim was to identify the characteristics of FoF in Parkinsons disease and assess its impact on QoL. We assessed FoF in 130 patients with Parkinson’s disease (PD) on scales measuring perceived self-efficacy in performing a range of activities (FES), perceived consequences of falling (CoF), and activity avoidance (SAFFE). A significant difference was found in FoF between PD patients who had previously fallen and those who had not and between frequent and infrequent fallers. Patient-rated disability significantly influenced FoF. Difficulty in rising from a chair, difficulty turning, start hesitation, festination, loss of balance, and shuffling were the specific mobility problems which were associated with greater FoF in PD. Disability was the main predictor of FoF, additionally depression predicted perceived consequences of falling, while anxiety predicted activity avoidance. The FoF measures explained 65% of the variance of QoL in PD, highlighting the clinical importance of FoF. These results have implications for the clinical management of FoF in PD.

Laughter induction techniques suitable for generating motion capture data of laughter associated body movements

Laughter is a frequently occurring social signal and an important part of human non-verbal communication. However it is often overlooked as a serious topic of scientific study. While the lack of research in this area is mostly due to laughters non-serious nature, it is also a particularly difficult social signal to produce on demand in a convincing manner; thus making it a difficult topic for study in laboratory settings. In this paper we provide some techniques and guidance for inducing both hilarious laughter and conversational laughter. These techniques were devised with the goal of capturing motion information related to laughter while the person laughing was either standing or seated. Comments on the value of each of the techniques and general guidance as to the importance of atmosphere, environment and social setting are provided.

MMLI: Multimodal Multiperson Corpus of Laughter in Interaction

The aim of the Multimodal and Multiperson Corpus of Laughter in Interaction (MMLI) was to collect multimodal data of laughter with the focus on full body movements and different laughter types. It contains both induced and interactive laughs from human triads. In total we collected 500 laugh episodes of 16 participants. The data consists of 3D body position information, facial tracking, multiple audio and video channels as well as physiological data. In this paper we discuss methodological and technical issues related to this data collection including techniques for laughter elicitation and synchronization between different independent sources of data. We also present the enhanced visualization and segmentation tool used to segment captured data. Finally we present data annotation as well as preliminary results of the analysis of the nonverbal behavior patterns in laughter.

Perception and Automatic Recognition of Laughter from Whole-Body Motion: Continuous and Categorical Perspectives

Despite its importance in social interactions, laughter remains little studied in affective computing. Intelligent virtual agents are often blind to users’ laughter and unable to produce convincing laughter themselves. Respiratory, auditory, and facial laughter signals have been investigated but laughter-related body movements have received less attention. The aim of this study is threefold. First, to probe human laughter perception by analyzing patterns of categorisations of natural laughter animated on a minimal avatar. Results reveal that a low dimensional space can describe perception of laughter “types”. Second, to investigate observers’ perception of laughter (hilarious, social, awkward, fake, and non-laughter) based on animated avatars generated from natural and acted motion-capture data. Significant differences in torso and limb movements are found between animations perceived as laughter and those perceived as non-laughter. Hilarious laughter also differs from social laughter. Different body movement features were indicative of laughter in sitting and standing avatar postures. Third, to investigate automatic recognition of laughter to the same level of certainty as observers’ perceptions. Results show recognition rates of the Random Forest model approach human rating levels. Classification comparisons and feature importance analyses indicate an improvement in recognition of social laughter when localized features and nonlinear models are used.

Multimodal Analysis of Laughter for an Interactive System

In this paper, we focus on the development of new methods to detect and analyze laughter, in order to enhance human-computer interactions. First, the general architecture of such a laughter-enabled application is presented. Then, we propose the use of two new modalities, namely body movements and respiration, to enrich the audiovisual laughter detection and classification phase. These additional signals are acquired using easily constructed affordable sensors. Features to characterize laughter from body movements are proposed, as well as a method to detect laughter from a measure of thoracic circumference.

Laugh When You’re Winning

Developing virtual characters with naturalistic game playing capabilities is an increasingly researched topic in Human-Computer Interaction. Possible roles for such characters include virtual teachers, personal care assistants, and companions for children. Laughter is an under-investigated emotional expression both in Human-Human and Human-Computer Interaction. The EU Project ILHAIRE, aims to study this phenomena and endow machines with laughter detection and synthesis capabilities. The Laugh when you’re winning project, developed during the eNTERFACE 2013 Workshop in Lisbon, Portugal, aimed to set up and test a game scenario involving two human participants and one such virtual character. The game chosen, the yes/no game, induces natural verbal and non-verbal interaction between participants, including frequent hilarious events, e.g., one of the participants saying “yes” or “no” and so losing the game. The setup includes software platforms, developed by the ILHAIRE partners, allowing automatic analysis and fusion of human participants’ multimodal data (voice, facial expression, body movements, respiration) in real-time to detect laughter. Further, virtual characters endowed with multimodal skills were synthesised in order to interact with the participants by producing laughter in a natural way.

Human Perception of Laughter from Context-Free Whole Body Motion Dynamic Stimuli

Laughter is a ubiquitous social signal in human interactions yet it remains understudied from a scientific point of view. The need to understand laughter and its role in human interactions has become more pressing as the ability to create conversational agents capable of interacting with humans has come closer to a reality. This paper reports on three aspects of the human perception of laughter when context has been removed and only the body information from the laughter episode remains. We report on ability to categorise the laugh type and the sex of the laugher, the relationship between personality factors with laughter categorisation and perception, and finally the importance of intensity in the perception and categorisation of laughter.