Gait pattern can alter aesthetic visual impression from a third-person perspective.

Beauty is related to our lives in various ways and examining it from an interdisciplinary approach is essential. People are very concerned with their appearance. A widely accepted beauty ideal is that the thinner an individual is, the more beautiful they are. However, the effect of continuous motion on body form aesthetics is unclear. Additionally, an upright pelvic posture in the sagittal plane during walking seems to affect the aesthetic judgments of female appearance. We directly analyzed the influence of body form and walking pattern on aesthetic visual impressions from a third-person perspective with a two-way analysis of variance. Captured motion data for three conditions-upright pelvis, normal pelvis, and posteriorly tilted pelvic posture-were applied to each of three mannequins, representing thin, standard, and obese body forms. When participants watched stimulus videos of the mannequins walking with various postures, a significantly higher score for aesthetic visual impression was noted for an upright pelvic posture than for a posteriorly tilted pelvic posture, irrespective of body form (F(2, 119) = 79.89, p < 0.001, η2 = 0.54). These findings show that the third-person perspective of beauty can be improved even without being thin by walking with an upright pelvic posture.

Using In-Shoe Inertial Measurement Unit Sensors to Understand Daily-Life Gait Characteristics in Patients With Distal Radius Fractures During 6 Months of Recovery: Cross-Sectional Study.

BACKGROUND: A distal radius fracture (DRF) is a common initial fragility fracture among women in their early postmenopausal period, which is associated with an increased risk of subsequent fractures. Gait assessments are valuable for evaluating fracture risk; inertial measurement units (IMUs) have been widely used to assess gait under free-living conditions. However, little is known about long-term changes in patients with DRF, especially concerning daily-life gait. We hypothesized that, in the long term, the daily-life gait parameters in patients with DRF could enable us to reveal future risk factors for falls and fractures. OBJECTIVE: This study assessed the spatiotemporal characteristics of patients with DRF at 4 weeks and 6 months of recovery. METHODS: We recruited 16 women in their postmenopausal period with DRF as their first fragility fracture (mean age 62.3, SD 7.0 years) and 28 matched healthy controls (mean age 65.6, SD 8.0 years). Daily-life gait assessments and physical assessments, such as hand grip strength (HGS), were performed using an in-shoe IMU sensor. Participants' results were compared with those of the control group, and their recovery was assessed for 6 months after the fracture. RESULTS: In the fracture group, at 4 weeks after DRF, lower foot height in the swing phase (P=.049) and higher variability of stride length (P=.03) were observed, which improved gradually. However, the dorsiflexion angle in the fracture group tended to be lower consistently during 6 months (at 4 weeks: P=.06; during 6 months: P=.07). As for the physical assessments, the fracture group showed lower HGS at all time points (at 4 weeks: P<.001; during 6 months: P=.04), despite significant improvement at 6 months (P<.001). CONCLUSIONS: With an in-shoe IMU sensor, we discovered the recovery of spatiotemporal gait characteristics 6 months after DRF surgery without the participants' awareness. The consistently unchanged dorsiflexion angle in the swing phase and lower HGS could be associated with fracture risk, implying the high clinical importance of appropriate interventions for patients with DRF to prevent future fractures. These results could be applied to a screening tool for evaluating the risk of falls and fractures, which may contribute to constructing a new health care system using wearable devices in the near future.

Gait characteristics in patients with distal radius fracture using an in-shoe inertial measurement system at various gait speeds.

BACKGROUND: Distal radius fractures (DRF) commonly occur in early postmenopausal females as the first fragility fracture. Although the incidence of DRF in this set of patients may be related to a lower ability to control their balance and gait, the detailed gait characteristics of DRF patients have not been examined. RESEARCH QUESTION: Is it possible to identify the physical and gait features of DRF patients using in-shoe inertial measurement unit (IMU) sensors at various gait speeds and to develop a machine learning (ML) algorithm to estimate patients with DRF using gait? METHODS: In this cross-sectional case control study, we recruited 28 postmenopausal females with DRF as their first fragility fracture and 32 age-matched females without a history of fragility fractures. The participants underwent several physical and gait tests. In the gait performance test, the participants walked 16 m with the in-shoe IMU sensor at slower, preferred, and faster speeds. The gait parameters were calculated by the IMU, and we applied the ML technique using the extreme gradient boosting (XGBoost) algorithm to predict the presence of DRF. RESULTS: The fracture group showed lower hand grip strength and lower ability to change gait speed. The difference in gait parameters was mainly observed at faster speeds. The amplitude of the change in the parameters was small in the fracture group. The XGBoost model demonstrated reasonable accuracy in predicting DRFs (area under the curve: 0.740), and the most relevant variable was the stance time at a faster speed. SIGNIFICANCE: Gait analysis using in-shoe IMU sensors at different speeds is useful for evaluating the characteristics of DRFs. The obtained gait parameters allow the prediction of fractures using the XGBoost algorithm.

A Successful Infliximab Treatment of a Pediatric Case of Severe Polyarteritis Nodosa With a Cerebral Infarction and a Decreased Adenosine Deaminase 2 Activity.

Polyarteritis nodosa (PAN) is a systemic necrotizing vasculitis common in males over 50 years of age that causes various organ symptoms. In recent years, it has become important to distinguish deficiency of adenosine deaminase 2 (DADA2) from childhood-onset PAN. A 13-year-old girl was urgently transferred to our hospital with sudden weakness in her right upper and lower limbs. The National Institutes of Health Stroke Scale (NIHSS) was 8. Plain MRI of the brain indicated high-signal areas in the right caudate nucleus, internal capsule, and left basal ganglia when applying T2-weighted, fluid-attenuated inversion recovery (FLAIR), and diffusion-weighted imaging (DWI); and low signals in the same regions in an apparent diffusion coefficient (ADC) map. It demonstrated inflammatory demyelinating disease of the central nervous system or multiple cerebral infarctions attributable to vasculitis, and it is difficult to differentiate between them based on image findings alone, and cannot be determined without following the clinical course. Hence, we treated with steroid therapy, which is effective for both conditions. Although the paralysis was alleviated, an MRI of the brain reperformed on day 7 revealed expansion of the lesion with contrast enhancement in the feeding area of the left lateral striatal artery, a high signal in DWI, and a low signal in an ADC map. Based on the clinical and radiological findings, we diagnosed a cerebral infarction attributable to vasculitis. Contrast computed tomography (CT) of her chest and abdominal CT angiography revealed that she met the diagnostic criteria for PAN, and adenosine deaminase 2 (AD2) activity level was low. The patient was treated with steroids combined with azathioprine and cyclophosphamide but three weeks after discharge developed a new cerebral infarction in the right basal ganglia. We commenced infliximab; no recurrence of cerebral infarction has been noted. The low AD2 activity may explain the intractable atypical course of this case. Further studies are needed to reveal the role of AD2 in patients with residual enzyme activity and reevaluation of the PAN diagnostic criteria is essential.

Fluctuations in Upper and Lower Body Movement during Walking in Normal Pressure Hydrocephalus and Parkinson's Disease Assessed by Motion Capture with a Smartphone Application, TDPT-GT.

We aimed to capture the fluctuations in the dynamics of body positions and find the characteristics of them in patients with idiopathic normal pressure hydrocephalus (iNPH) and Parkinson's disease (PD). With the motion-capture application (TDPT-GT) generating 30 Hz coordinates at 27 points on the body, walking in a circle 1 m in diameter was recorded for 23 of iNPH, 23 of PD, and 92 controls. For 128 frames of calculated distances from the navel to the other points, after the Fourier transforms, the slopes (the representatives of fractality) were obtained from the graph plotting the power spectral density against the frequency in log-log coordinates. Differences in the average slopes were tested by one-way ANOVA and multiple comparisons between every two groups. A decrease in the absolute slope value indicates a departure from the 1/f noise characteristic observed in healthy variations. Significant differences in the patient groups and controls were found in all body positions, where patients always showed smaller absolute values. Our system could measure the whole body's movement and temporal variations during walking. The impaired fluctuations of body movement in the upper and lower body may contribute to gait and balance disorders in patients.

Foot characteristics of the daily-life gait in postmenopausal females with distal radius fractures: a cross-sectional study.

BACKGROUND: Gait decline in older adults is related to falling risk, some of which contribute to injurious falls requiring medical attention or restriction of activity of daily living. Among injurious falls, distal radius fracture (DRF) is a common initial fragility fracture associated with the subsequent fracture risk in postmenopausal females. The recent invention of an inertial measurement unit (IMU) facilitates the assessment of free-living gait; however, little is known about the daily gait characteristics related to the risk of subsequent fractures. We hypothesized that females with DRF might have early changes in foot kinematics in daily gait. The aim of this study was to evaluate the daily-life gait characteristics related to the risk of falls and fracture. METHODS: In this cross-sectional study, we recruited 27 postmenopausal females with DRF as their first fragility fracture and 28 age-matched females without a history of fragility fractures. The participants underwent daily gait assessments for several weeks using in-shoe IMU sensors. Eight gait parameters and each coefficient of variance were calculated. Some physical tests, such as hand grip strength and Timed Up and Go tests, were performed to check the baseline functional ability. RESULTS: The fracture group showed lower foot angles of dorsiflexion and plantarflexion in the swing phase. The receiver operating characteristic curve analyses revealed that a total foot movement angle (TFMA) < 99.0 degrees was the risk of subsequent fracture. CONCLUSIONS: We extracted the daily-life gait characteristics of patients with DRF using in-shoe IMU sensors. A lower foot angle in the swing phase, TFMA, may be associated with the risk of subsequent fractures, which may be effective in evaluating future fracture risk. Further studies to predict and prevent subsequent fractures from daily-life gait are warranted.

HFA analysis using scalp electroencephalograms in two cases of Rasmussen's syndrome.

OBJECTIVE: In recent years, wide-band EEGs have been used to assess brain activity, and their effectiveness in the pathological analysis of epilepsy has been demonstrated. This report describes two cases of Rasmussen's syndrome (RS) in which high-frequency scalp EEGs were retrospectively analyzed to assess the pathological condition of epilepsy in RS. METHODS: The two RS cases were divided into three periods: incipient, stable, and frequent seizure periods. Using the EEG record of each period, interictal epileptiform discharges (IEDs) were visually extracted. Subsequently, a time-frequency analysis was performed to calculate the rate of high-frequency activities (HFAs) (IED-HFA rate). Finally, differences between the three periods were examined. RESULTS: IED-HFA rates significantly increased in the frequent seizure period compared with the stable period in both cases(P < 0.05). CONCLUSION: there was a significant increase in HFAs superimposed over IEDs during the frequent seizure period compared to the stable period. HFAs are thought to be associated with epileptogenicity. Similarly, HFAs could be a useful biomarker for the pathological condition of epilepsy in RS.

Artificial Intelligence Distinguishes Pathological Gait: The Analysis of Markerless Motion Capture Gait Data Acquired by an iOS Application (TDPT-GT).

Distinguishing pathological gait is challenging in neurology because of the difficulty of capturing total body movement and its analysis. We aimed to obtain a convenient recording with an iPhone and establish an algorithm based on deep learning. From May 2021 to November 2022 at Yamagata University Hospital, Shiga University, and Takahata Town, patients with idiopathic normal pressure hydrocephalus (n = 48), Parkinson's disease (n = 21), and other neuromuscular diseases (n = 45) comprised the pathological gait group (n = 114), and the control group consisted of 160 healthy volunteers. iPhone application TDPT-GT captured the subjects walking in a circular path of about 1 meter in diameter, a markerless motion capture system, with an iPhone camera, which generated the three-axis 30 frames per second (fps) relative coordinates of 27 body points. A light gradient boosting machine (Light GBM) with stratified k-fold cross-validation (k = 5) was applied for gait collection for about 1 min per person. The median ability model tested 200 frames of each person's data for its distinction capability, which resulted in the area under a curve of 0.719. The pathological gait captured by the iPhone could be distinguished by artificial intelligence.

Identification of characteristics of foot position and angle during swing phase in fallers using principal component analysis.

Identifying the characteristics of fallers is important for preventing falls because such events may reduce quality of life. It has been reported that several variables related to foot positions and angles during gait (e.g., sagittal foot angle and minimum toe clearance) differ between fallers and non-fallers. However, examining such representative discrete variables may not be sufficient to detect crucial information, which may be contained in the large portions of unanalyzed data. Therefore, we aimed to identify the comprehensive characteristics of foot position and angle during the swing phase of gait in non-fallers and fallers using principal component analysis (PCA). Thirty non-fallers and 30 fallers were recruited for this study. We performed PCA to reduce the dimensions of foot positions and angles during the swing phase and obtained principal component scores (PCSs) for each principal component vector (PCV), which were then compared between groups. The results revealed that the PCS of PCV3 in fallers was significantly larger than that in non-fallers (p = 0.003, Cohen's d = 0.80). We reconstructed waveforms of foot positions and angles during the swing phase using PCV3 and our main findings can be summarized as follows. Compared to non-fallers, fallers have a 1) low average foot position in the z-axis (i.e., height) during the initial swing phase 2) small average foot angle in the x-axis (i.e., rotation in the sagittal plane), during the initial swing phase, and 3) large variability in foot position in the y-axis (i.e., anterior/posterior position) during the initial swing phase. We can conclude that these are characteristics of gait related to fallers. Therefore, our findings may be beneficial for evaluating fall risk during gait using a device such as a shoe- or insole-embedded inertial measurement unit.

Development of a data platform for monitoring personal health records in Japan: The Sustaining Health by Integrating Next-generation Ecosystems (SHINE) Study.

BACKGROUND: The Sustaining Health by Integrating Next-generation Ecosystems (SHINE) Study was developed as a data platform that incorporates personal health records (PHRs) into health-related data at the municipal level in Japan. This platform allows analyses of the associations between PHRs and future health statuses, and supports the production of evidence for developing preventive care interventions. Herein, we introduce the SHINE Study's profile and describe its use in preliminary analyses. METHODS: The SHINE Study involves the collection of participants' health measurements and their addition to various health-related data from the Longevity Improvement & Fair Evidence (LIFE) Study. With cooperation from municipal governments, measurements can be acquired from persons enrolled in government-led long-term care prevention classes and health checkups who consent to participate in the SHINE Study. For preliminary analyses, we collected salivary test measurements, lifelog measurements, and gait measurements; these were linked with the LIFE Study's database. We analyzed the correlations between these measurements and the previous year's health care expenditures. RESULTS: We successfully linked PHR data of 33 participants for salivary test measurements, 44 participants for lifelog measurements, and 32 participants for gait measurements. Only mean torso speed in the gait measurements was significantly correlated with health care expenditures (r = -0.387, P = 0.029). CONCLUSION: The SHINE Study was developed as a data platform to collect and link PHRs with the LIFE Study's database. The analyses undertaken with this platform are expected to contribute to the development of preventive care tools and promote health in Japan.

Quantitative Gait Feature Assessment on Two-Dimensional Body Axis Projection Planes Converted from Three-Dimensional Coordinates Estimated with a Deep Learning Smartphone App.

To assess pathological gaits quantitatively, three-dimensional coordinates estimated with a deep learning model were converted into body axis plane projections. First, 15 healthy volunteers performed four gait patterns; that is, normal, shuffling, short-stepped, and wide-based gaits, with the Three-Dimensional Pose Tracker for Gait Test (TDPT-GT) application. Second, gaits of 47 patients with idiopathic normal pressure hydrocephalus (iNPH) and 92 healthy elderly individuals in the Takahata cohort were assessed with the TDPT-GT. Two-dimensional relative coordinates were calculated from the three-dimensional coordinates by projecting the sagittal, coronal, and axial planes. Indices of the two-dimensional relative coordinates associated with a pathological gait were comprehensively explored. The candidate indices for the shuffling gait were the angle range of the hip joint < 30° and relative vertical amplitude of the heel < 0.1 on the sagittal projection plane. For the short-stepped gait, the angle range of the knee joint < 45° on the sagittal projection plane was a candidate index. The candidate index for the wide-based gait was the leg outward shift > 0.1 on the axial projection plane. In conclusion, the two-dimensional coordinates on the body axis projection planes calculated from the 3D relative coordinates estimated by the TDPT-GT application enabled the quantification of pathological gait features.

Evaluation of cerebrospinal fluid biomarkers in pediatric patients with spinal muscular atrophy.

BACKGROUND: Spinal muscular atrophy (SMA) is a rare neuromuscular disorder characterised by muscle weakness and muscle atrophy and classified into five known subtypes based on clinical features. The recent development of novel drugs to treat SMA has been encouraging, and nusinersen is the first drug approved to treat SMA. OBJECTIVE: To explore cerebrospinal fluid (CSF) biomarkers of SMA and investigate their relationship with symptoms and the treatment response in pediatric patients. METHODS: We analyzed the CSF levels of chitotriosidase 1 (CHIT1) and inflammatory cytokines (tumor necrosis factor [TNF]-α and interferon [INF]-γ) using enzyme-linked immunosorbent assays in pediatric SMA patients treated at Hiroshima University Hospital over 2 years. RESULTS: This study analyzed pediatric SMA patients. While the CSF inflammatory cytokines (TNF-α and INF-γ) in these SMA children were unchanged, the CHIT1 levels decreased significantly from year 1 to 2 of treatment. We also found a trend toward an inverse correlation between the motor function score (HINE-2 scores) and CHIT1 level from year 1 to 2 of treatment. CONCLUSIONS: CHIT1 may be a CSF biomarker of the treatment response in pediatric SMA.

ATYPICAL ENDOGENOUS FUNGAL ENDOPHTHALMITIS CAUSED BY CANDIDA RUGOSA.

PURPOSE: The purpose of this study was to report a case of atypical endogenous fungal endophthalmitis caused by Candida rugosa , a rare species of nonalbicans Candida . METHODS: This report describes a case of a 45-year-old woman who presented with a reduced visual acuity in the right eye in addition to vitreous opacity during breast cancer treatment, which was suspected as fungal endophthalmitis from medical examination and history. Various tests were performed for diagnosis. RESULTS: Blood test results were normal, including the blood beta-D-glucan level, and blood cultures were negative. Diagnosis could not be made using systemic computed tomography and magnetic resonance imaging results. Therefore, a lesion sample was collected by using vitrectomy. C. rugosa was identified through DNA (extracted from the lesion sample) analysis using Basic Local Alignment Search Tool. The visual acuity of the right eye improved after vitrectomy. CONCLUSION: We encountered a rare case of atypical endogenous fungal endophthalmitis caused by C. rugosa . Clinicians sometimes encounter invasive candidiasis caused by rare nonalbicans Candida species. DNA analysis using Basic Local Alignment Search Tool is effective for diagnosing such cases.

Effect of Lacosamide therapy on blood cells and IgA levels in children and adolescents with epilepsy in a clinical setting.

INTRODUCTION: Lacosamide (LCM) is a third-generation antiepileptic drug (AED) that affects sodium channel inactivation. AEDs can affect multiple organ systems and blood parameters. Carbamazepine (CBZ) reportedly affects blood sodium, lipid, and immunoglobulin levels and thyroid function. Despite multiple studies on the adverse effects of AEDs, few reports have discussed the impact of LCM on blood parameters. The purpose of this study was to clarify the effects of LCM on blood parameters. METHODS: We retrospectively examined the medical records of 15 children and adolescents in whom LCM was initiated between April 2017 and March 2021, 6 and 12 months after treatment initiation. Blood cell counts, biochemical and thyroid function, and immunoglobulin levels were investigated at baseline and 6 and 12 months after initiation of LCM. RESULTS: Neutrophil levels were significantly reduced 12 months after LCM initiation (p = 0.0046); however, the value was not abnormal. Immunoglobulin A was significantly elevated 6 and 12 months after LCM initiation (p = 0.0078 and 0.020, respectively). No significant difference was identified in the other parameters. Electrolyte and lipid levels and thyroid function remained unaffected, unlike with CBZ. CONCLUSIONS: LCM may affect the immune system, as well as hematological parameters. Further investigation with larger samples is required in the future to assess the clinical impact.

Age Differences in the Interpretation of Facial Emojis: Classification on the Arousal-Valence Space.

Emojis are universal tools that are frequently used to express people's emotional states throughout daily communications. They are often applied in various fields of research, such as consumer surveys, as indicators of users' emotional states. Further analyses of emoji interpretation among people with age are required to ensure the validity of emojis as a metric in such fields of research, thereby reducing misunderstandings. However, details regarding the effect of age on both arousal and valence, as they pertain to the interpretation of emojis, remain unclear. Therefore, in this study, we investigate the effects of the interpretation of facial emojis on the arousal-valence space among people of varying age groups. We conducted an online survey involving 2,000 participants, whereby we employed a nine-point scale to evaluate the valence and arousal levels associated with 74 facial emojis. Based on the two axes of valence and arousal among the age groups involved in this study, emojis are categorized into six similar clusters. For the two negative clusters, i.e., strongly negative and moderately negative sentiments, the group involving middle-aged participants showed significantly higher levels of arousal compared to the group involving young participants. Additionally, not all emojis classified into the aforementioned negative clusters indicate age difference. Based on these results, this study recommends using emojis with no age-related effects on the negative clusters as indices for evaluating human emotions.

Development of Smartphone Application for Markerless Three-Dimensional Motion Capture Based on Deep Learning Model.

To quantitatively assess pathological gait, we developed a novel smartphone application for full-body human motion tracking in real time from markerless video-based images using a smartphone monocular camera and deep learning. As training data for deep learning, the original three-dimensional (3D) dataset comprising more than 1 million captured images from the 3D motion of 90 humanoid characters and the two-dimensional dataset of COCO 2017 were prepared. The 3D heatmap offset data consisting of 28 × 28 × 28 blocks with three red-green-blue colors at the 24 key points of the entire body motion were learned using the convolutional neural network, modified ResNet34. At each key point, the hottest spot deviating from the center of the cell was learned using the tanh function. Our new iOS application could detect the relative tri-axial coordinates of the 24 whole-body key points centered on the navel in real time without any markers for motion capture. By using the relative coordinates, the 3D angles of the neck, lumbar, bilateral hip, knee, and ankle joints were estimated. Any human motion could be quantitatively and easily assessed using a new smartphone application named Three-Dimensional Pose Tracker for Gait Test (TDPT-GT) without any body markers or multipoint cameras.

Kinematic characteristics during gait in frail older women identified by principal component analysis.

Frailty is associated with gait variability in several quantitative parameters, including high stride time variability. However, the associations between joint kinematics during walking and increased gait variability with frailty remain unclear. In the current study, principal component analysis was used to identify the key joint kinematics characteristics of gait related to frailty. We analyzed whole kinematic waveforms during the entire gait cycle obtained from the pelvis and lower limb joint angle in 30 older women (frail/prefrail: 15 participants; non-frail: 15 participants). Principal component analysis was conducted using a 60 × 1224 input matrix constructed from participants' time-normalized pelvic and lower-limb-joint angles along three axes (each leg of 30 participants, 51 time points, four angles, three axes, and two variables). Statistical analyses revealed that only principal component vectors 6 and 9 were related to frailty. Recombining the joint kinematics corresponding to these principal component vectors revealed that frail older women tended to exhibit greater variability of knee- and ankle-joint angles in the sagittal plane while walking compared with non-frail older women. We concluded that greater variability of knee- and ankle-joint angles in the sagittal plane are joint kinematic characteristics of gait related to frailty.

Experimental Verification of Micro-Doppler Radar Measurements of Fall-Risk-Related Gait Differences for Community-Dwelling Elderly Adults.

In a previous study, we developed a classification model to detect fall risk for elderly adults with a history of falls (fallers) using micro-Doppler radar (MDR) gait measurements via simulation. The objective was to create daily monitoring systems that can identify elderly people with a high risk of falls. This study aimed to verify the effectiveness of our model by collecting actual MDR data from community-dwelling elderly people. First, MDR gait measurements were performed in a community setting, and the efficient gait parameters for the classification of fallers were extracted. Then, a support vector machine model that was trained and validated using the simulated MDR data was tested for the gait parameters extracted from the actual MDR data. A classification accuracy of 78.8% was achieved for the actual MDR data. The validity of the experimental results was confirmed based on a comparison with the results of our previous simulation study. Thus, the practicality of the faller classification model constructed using the simulated MDR data was verified for the actual MDR data.