Case Report: Accessible Digital Musical Instrument Can Be Used for Active Music Therapy in a Person With Severe Dementia and Worsening Behavioral and Psychological Symptoms: A Case Study Over a Year and a Half.

Despite the fact that accessible digital musical instruments can take into account the level of cognitive demands, previous studies have been conducted with patients with mild cognitive impairment (MCI), and it is not known whether they can be used by people with moderate to severe dementia or dementia with worsening behavioral and psychological symptoms of dementia (BPSD). The participant was an 88-year-old woman with vascular dementia (VaD) (Mini-Mental State Examination [MMSE] and Neuropsychiatric Inventory [NPI] scores: 8 and 20, respectively). Music therapy (MT) was provided twice a week for 15 min, and MT sessions spanned over 18 months. For the MT, we used the cyber musical instrument with score (Cymis), an accessible digital musical instrument; it could be played using a touch panel and switches. The cognitive function of the participant declined further, with MMSE scores of 4 after 1 year and 0 after 1.5 years. BPSD peaked with the NPI score of 54 at 1 year and declined thereafter, although only apathy remained. Despite these changes, during MT, she was able to play the accessible digital musical instrument and focus on the performance. These results suggest that even patients with severe VaD can play an accessible digital instrument and continue active music therapy even if their BPSD progress with cognitive decline.

Parkinsonian rigidity shows variable properties depending on the elbow joint angle.

Parkinsonian rigidity has been thought to be constant through a full range of joint angle. The aim of this study was to perform a detailed investigation of joint angle dependency of rigidity. We first measured muscle tone at the elbow joint in 20 healthy subjects and demonstrated that an angle of approximately 60° of flexion marks the division of two different angle-torque characteristics. Then, we measured muscle tone at the elbow joint in 24 Parkinson's Disease (PD) patients and calculated elastic coefficients in flexion and extension in the ranges of 10°-60° (distal) and 60°-110° (proximal). Rigidity as represented by the elastic coefficient in the distal phase of elbow joint extension was best correlated with the UPDRS rigidity score (r = 0.77). A significant difference between the UPDRS rigidity score 0 group and 1 group was observed in the elastic coefficient in the distal phase of extension (P < 0.0001), whereas no significant difference was observed in the proximal phase of extension and in each phase of flexion. Parkinsonian rigidity shows variable properties depending on the elbow joint angle, and it is clearly detected at the distal phase of elbow extension.

A novel method for systematic analysis of rigidity in Parkinson's disease.

We propose a novel system that analyzes the components of rigidity in Parkinson's disease (PD) usually perceived by physicians, in a very simple and systematic way for routine clinical practice. Our system is composed of two compact force sensors, a gyroscope, and EMG surface electrodes. Muscle tone was assessed in 24 healthy elderly subjects and 27 PD patients by passive extension and flexion of the elbow joint with ramp-and-hold trajectory. Torque and angle data in the dynamic phase were used to calculate "elastic coefficients" in extension and flexion, and the mean value of torque in each dynamic phase at each joint angle (defined as "Bias") was also calculated. The muscle activity index in the static phase (EMG Index) was obtained for both biceps brachii (BB) and triceps brachii (TB) muscles. "Elastic coefficients," sum of the "difference of Bias" and "EMG Index" for BB all correlated well with UPDRS score. Based on our results, Parkinsonian rigidity consists of both an "elastic" component and a "difference of Bias" component, and neurologists may assess greater rigidity when either one or both components are high in value. The EMG index was useful for differentiating PD patients with slight rigidity from healthy elderly adults.

Analysis of spatial temporal plantar pressure pattern during gait in Parkinson's disease.

Spatial temporal plantar pressure patterns measured with sheet-shaped pressure sensor were investigated to extract features of gait in Parkinson's disease. Both six subjects of Parkinson's disease (PD) and elderly fourteen normal control subjects were asked to execute usual walking on the pressure sensor sheets. Candidate features were step length, step time, gait velocity and transition of center of pressure to foot axis direction. The step length and gait velocity were smaller in PD subjects than those in normal subjects. Time of step cycle in three PD subjects were longer than that in normal subjects while the times of other PD subjects were similar to those of control subjects. The length from heel contact to toe off within one footprint was small in the subjects with short step length. Such possibility was indicated that Parkinson's disease in gait could be separated from normal subjects by these features.

Measurement system of finger-tapping contact force for quantitative diagnosis of Parkinson's disease.

The purpose of this study was to develop a measuring system of contact force in finger-tapping of Parkinson's disease patients and to show its effectiveness for quantitative diagnosis. This system was composed of a pair of 3-axis accelerometers, a touch sensor an analog to digital converter and a personal computer (PC). Firstly, a transfer function representing relation between the contact force and the accelerometer output during the finger contact phase of finger-tapping was determined. This means that the finger-tapping contact force could be estimated from the measured acceleration by using the determined transfer function. Secondly the developed system was applied to 27 normal subjects and 16 Parkinson's diseases subjects. Score of UPDRS finger tap test was evaluated for each subject by a neurologist. Finally, these sensors were attached to subject's index finger and thumb, and sensor signals were recorded and processed within the PC. The subjects were asked to execute continuous finger taps movement for 60 s. It was shown that the contact force was smaller as the subject was with the larger UPDRS score of tap test.

Finger taps movement acceleration measurement system for quantitative diagnosis of Parkinson's disease.

The purpose of this study was to develop a finger taps acceleration measurement system for the quantitative diagnosis of Parkinson's disease. The system was composed of two 3-axis piezoelectric element accelerometers, a pair of touch sensors made of thin stainless steel sheets, an analog-digital(AD) converter and a personal computer (PC). Fingerstalls,with these sensors, were attached to subject's index finger and thumb. The acceleration and output of the touch sensors were recorded using the PC during the finger taps movements. Intervals between the single finger taps movements were calculated from the measured output of the touch sensors. Velocities during the single finger taps movements were calculated by integrating the measured acceleration. The amplitudes were calculated by integrating the velocities. The standard deviation of the single finger taps intervals, average of maximum single finger taps velocities and average of maximum single finger taps amplitudes were calculated from them. They were used as features for the quantitative diagnosis of Parkinson's disease. The developed system was used to conduct finger taps tests employing 27 normal subjects and 16 Parkinson's diseases subjects. The subjects were asked to execute continuous finger taps movement for 60 s. It was shown that the acceleration and output of the touch sensors could be measured and the features could be extracted.

The application of independent component analysis to the multi-channel surface electromyographic signals for separation of motor unit action potential trains: part I-measuring techniques.

The purpose of this study is to examine whether or not the application of independent component analysis (ICA) is useful for separation of motor unit action potential trains (MUAPTs) from the multi-channel surface EMG (sEMG) signals. In this study, the eight-channel sEMG signals were recorded from tibialis anterior muscles during isometric dorsi-flexions at 5%, 10%, 15% and 20% maximal voluntary contraction. Recording MUAP waveforms with little time delay mounted between the channels were obtained by vertical sEMG channel arrangements to muscle fibers. The independent components estimated by FastICA were compared with the sEMG signals and the principal components calculated by principal component analysis (PCA). From our results, it was shown that FastICA could separate groups of similar MUAP waveforms of the sEMG signals separated into each independent component while PCA could not sufficiently separate the groups into the principal components. A greater reduction of interferences between different MUAP waveforms was demonstrated by the use of FastICA. Therefore, it is suggested that FastICA could provide much better discrimination of the properties of MUAPTs for sEMG signal decomposition, i.e. waveforms, discharge intervals, etc., than not only PCA but also the original sEMG signals.

The application of independent component analysis to the multi-channel surface electromyographic signals for separation of motor unit action potential trains: part II-modelling interpretation.

The purpose of this article was to investigate whether or not FastICA can separate identical motor unit action potential trains (MUAPTs) of the 8-channel surface electromyographic (sEMG) signals constructed by an sEMG model into the independent components. Firstly, we have examined how much the increase of motor units (MUs) in the simulated sEMG signals influenced the performance on the separation of MUAPTs by kurtosis. The decreased trend of mean kurtosis on both sEMG signals and their independent components were observed as MUs were increased. These data suggested that the separation performance decayed when MUs were increased. Secondary, the differences between the independent components and the principal components have been also applied to the simulated sEMG signals with or without time delay between the sEMG channels. FastICA could successfully separate identical MUAPTs with no time delay but principal component analysis (PCA) could not do so. Against it, both FastICA and PCA could not separate MUAPTs with some time delay. In conclusion, our results suggested that FastICA could separate identical MUAPTs with no time delay into the independent components by FastICA, which might offer a new technique for the separation of interfered MUAP waveforms based on statistical properties of sEMG signal distributions.