1
|
Hu Y, Chen J, Chen J, Wang W, Zhao S, Hu X. An Ensemble Classification Model for Depression Based on Wearable Device Sleep Data. IEEE J Biomed Health Inform 2024; 28:2602-2612. [PMID: 37030745 DOI: 10.1109/jbhi.2023.3258601] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/10/2023]
Abstract
Depression is one of the most common mental disorders, with sleep disturbances as typical symptoms. With the popularity of wearable devices increasing in recent years, more and more people wear portable devices to track sleep quality. Based on this, we believe that depression detection through wearable sleep data is more intelligent and economical. However, the majority of wearable devices face the problem of missing data during the data collection process. Otherwise, most existing studies of depression identification focus on the utilization of complex data, making it difficult to generalize and susceptible to noise interference. To address these issues, we propose a systematic ensemble classification model for depression (ECD). For the missing data problem of wearable devices, we design an improved GAIN method to further control the generation range of interpolated values, which can achieve a more reasonable treatment of missing values. Compared with the original GAIN approach, the improved method shows a 28.56% improvement when using MAE as the metric. For depression recognition, we use ensemble learning to construct a depression classification model which combines five classification models, including SVM, KNN, LR, CBR, and DT. Ensemble learning can improve the model's robustness and generalization. The voting mechanism is used in several places to improve noise immunity. The final classification model performed great on the dataset, with a precision of 92.55% and a recall of 91.89%. These results illustrate how efficient this method is in automatically detecting depression.
Collapse
|
2
|
Lu H, Xu S, Zhao S, Hu X, Ma R, Hu B. EPIC: Emotion Perception by Spatio-Temporal Interaction Context of Gait. IEEE J Biomed Health Inform 2024; 28:2592-2601. [PMID: 37018306 DOI: 10.1109/jbhi.2022.3233597] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/03/2023]
Abstract
Recently, psychophysiological computing has received considerable attention. Due to easy acquisition at a distance and less conscious initiation, gait-based emotion recognition is considered as a valuable research branch in the field of psychophysiological computing. However, most existing methods rarely explore the spatio-temporal context of gait, which limits the ability to capture the higher-order relationship between emotion and gait. In this paper, we utilize a range of research, including psychophysiological computing and artificial intelligence, to propose an integrated emotion perception framework called EPIC, which can find novel joint topology and generate thousands of synthetic gaits by spatio-temporal interaction context. First, we analyze the joint coupling among non-adjacent joints by calculating Phase Lag Index (PLI), which can discover the latent connection among body joints. Second, to synthesize more sophisticated and accurate gait sequences, we explore the effect of spatio-temporal constraints, and propose a new loss function that utilizes the Dynamic Time Warping (DTW) algorithm and pseudo-velocity curve to constrain the output of Gated Recurrent Units (GRU). Finally, Spatial Temporal Graph Convolution Networks (ST-GCN) is used to classify emotions using the generation and the real data. Experimental results demonstrate our approach achieves the accuracy of 89.66%, and outperforms the state-of-the-art methods on Emotion-Gait dataset.
Collapse
|
3
|
Li X, Yu J, Jiang S, Fang L, Li Y, Ma S, Kong H, Qin X, Zhu D. Circadian rhythms of melatonin and its relationship with anhedonia in patients with mood disorders: a cross-sectional study. BMC Psychiatry 2024; 24:165. [PMID: 38413912 PMCID: PMC10900661 DOI: 10.1186/s12888-024-05606-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/25/2023] [Accepted: 02/12/2024] [Indexed: 02/29/2024] Open
Abstract
BACKGROUND Mood disorders are strongly associated with melatonin disturbances. However, it is unclear whether there is a difference in melatonin concentrations and melatonin circadian rhythm profiles between depression and bipolar disorder. In addition, the relationship between anhedonia, a common symptom of affective disorders, and its melatonin circadian rhythm remains under-investigated. METHODS Thirty-four patients with depression disorder, 20 patients diagnosed with bipolar disorder and 21 healthy controls participated in this study. The Revised Physical Anhedonia Scale (RPAS) was performed to assess anhedonia. Saliva samples were collected from all subjects at fixed time points (a total of 14 points) in two consecutive days for measuring the melatonin concentrations to fit circadian rhythms of subjects. Melatonin circadian rhythms were compared between the three groups using ANOVA. Partial correlation analysis and linear regression analysis were used to explore the correlation between melatonin rhythm variables and anhedonia. RESULTS We found that the peak phase of melatonin in the depression group was significantly advanced compared to the control group (P < 0.001) and the bipolar disorder group (P = 0.004). The peak phase of melatonin and RPAS showed a negative correlation (P = 0.003) in depression patients, which was also demonstrated in the multiple linear regression model (B=-2.47, P = 0.006). CONCLUSIONS These results suggest that circadian rhythms of melatonin are differentiated in depression and bipolar disorder and correlate with anhedonia in depression. Future research needs to explore the neurobiological mechanisms linking anhedonia and melatonin circadian rhythms in depressed patients.
Collapse
Affiliation(s)
- Xinyu Li
- The School of Mental Health and Psychological Sciences, Anhui Medical University, 230032, Hefei, China
- Department of Sleep Disorders, Anhui Mental Health Center, 230022, Hefei, China
- Department of Sleep Disorders, Hefei Fourth People's Hospital, 230022, Hefei, China
| | - Jiakuai Yu
- Department of Sleep Disorders, Anhui Mental Health Center, 230022, Hefei, China
- Department of Sleep Disorders, Hefei Fourth People's Hospital, 230022, Hefei, China
| | - Shuo Jiang
- Institutes of Physical Science and Information Technology, Anhui University, 230039, Hefei, China
| | - Liang Fang
- Department of Sleep Disorders, Anhui Mental Health Center, 230022, Hefei, China
- Department of Sleep Disorders, Hefei Fourth People's Hospital, 230022, Hefei, China
| | - Yifei Li
- The School of Mental Health and Psychological Sciences, Anhui Medical University, 230032, Hefei, China
- Department of Sleep Disorders, Anhui Mental Health Center, 230022, Hefei, China
- Department of Sleep Disorders, Hefei Fourth People's Hospital, 230022, Hefei, China
| | - Shuangshuang Ma
- School of Nursing, Anhui Medical University, 230032, Hefei, China
| | - Hui Kong
- The School of Mental Health and Psychological Sciences, Anhui Medical University, 230032, Hefei, China
- Department of Sleep Disorders, Anhui Mental Health Center, 230022, Hefei, China
- Department of Sleep Disorders, Hefei Fourth People's Hospital, 230022, Hefei, China
| | - Ximing Qin
- Institutes of Physical Science and Information Technology, Anhui University, 230039, Hefei, China.
| | - Daomin Zhu
- The School of Mental Health and Psychological Sciences, Anhui Medical University, 230032, Hefei, China.
- Department of Sleep Disorders, Anhui Mental Health Center, 230022, Hefei, China.
- Department of Sleep Disorders, Hefei Fourth People's Hospital, 230022, Hefei, China.
| |
Collapse
|
4
|
Ernst H, Scherpf M, Pannasch S, Helmert JR, Malberg H, Schmidt M. Assessment of the human response to acute mental stress-An overview and a multimodal study. PLoS One 2023; 18:e0294069. [PMID: 37943894 PMCID: PMC10635557 DOI: 10.1371/journal.pone.0294069] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2023] [Accepted: 10/24/2023] [Indexed: 11/12/2023] Open
Abstract
Numerous vital signs are reported in association with stress response assessment, but their application varies widely. This work provides an overview over methods for stress induction and strain assessment, and presents a multimodal experimental study to identify the most important vital signs for effective assessment of the response to acute mental stress. We induced acute mental stress in 65 healthy participants with the Mannheim Multicomponent Stress Test and acquired self-assessment measures (Likert scale, Self-Assessment Manikin), salivary α-amylase and cortisol concentrations as well as 60 vital signs from biosignals, such as heart rate variability parameters, QT variability parameters, skin conductance level, and breath rate. By means of statistical testing and a self-optimizing logistic regression, we identified the most important biosignal vital signs. Fifteen biosignal vital signs related to ventricular repolarization variability, blood pressure, skin conductance, and respiration showed significant results. The logistic regression converged with QT variability index, left ventricular work index, earlobe pulse arrival time, skin conductance level, rise time and number of skin conductance responses, breath rate, and breath rate variability (F1 = 0.82). Self-assessment measures indicated successful stress induction. α-amylase and cortisol showed effect sizes of -0.78 and 0.55, respectively. In summary, the hypothalamic-pituitary-adrenocortical axis and sympathetic nervous system were successfully activated. Our findings facilitate a coherent and integrative understanding of the assessment of the stress response and help to align applications and future research concerning acute mental stress.
Collapse
Affiliation(s)
- Hannes Ernst
- Institute of Biomedical Engineering, TU Dresden, Dresden, Germany
| | - Matthieu Scherpf
- Institute of Biomedical Engineering, TU Dresden, Dresden, Germany
| | - Sebastian Pannasch
- Chair of Engineering Psychology and Applied Cognitive Research, TU Dresden, Dresden, Germany
| | - Jens R. Helmert
- Chair of Engineering Psychology and Applied Cognitive Research, TU Dresden, Dresden, Germany
| | - Hagen Malberg
- Institute of Biomedical Engineering, TU Dresden, Dresden, Germany
| | - Martin Schmidt
- Institute of Biomedical Engineering, TU Dresden, Dresden, Germany
| |
Collapse
|
5
|
Homagain A, Ehgoetz Martens KA. Emotional states affect steady state walking performance. PLoS One 2023; 18:e0284308. [PMID: 37708145 PMCID: PMC10501668 DOI: 10.1371/journal.pone.0284308] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2023] [Accepted: 08/25/2023] [Indexed: 09/16/2023] Open
Abstract
Gait is a large component and indicator of health. Many factors affect gait including age, disease, and even mood disorders. Few studies have looked at the influence of emotional states on gait. This study aimed to investigate the influence of emotional states on walking performance to understand whether an emotional state may be an important factor to consider when evaluating gait. Thirty-six young adults were recruited (23F, 13M) and performed a neutral baseline condition of walking which included six passes of walking across an 8m walkway (a total of 48m of walking). Participants then completed 6 pseudo-randomized emotional state induction conditions while immersive 360-degree videos were used to induce the following emotional conditions: happiness, excitement, sadness, fear, and anger. Participants viewed the emotion elicitation videos using a virtual reality head-mounted display (HMD), then rated their emotional state using self-assessment manikins and walked (without the HMD) over a pressure sensor walkway. One-way repeated measures ANOVA and pairwise comparisons were used to examine differences in gait parameters across the emotional conditions. Participants walked with significantly reduced step length and speed during the sadness condition compared to the other emotional conditions and the neutral condition. Furthermore, participants adjusted the timing of their walking during the sadness condition and walked with significantly increased step, stance, and swing times compared to other emotional conditions, but not the neutral condition. Step time was significantly reduced during the conditions of excitement and fear compared to the neutral condition. Emotions may impact variety of gait parameters involving pace and rhythm, however have little influence on gait variability and postural control. These results indicate that perhaps the emotions of sadness and excitement should be taken into account as potential confounds for future gait analysis.
Collapse
Affiliation(s)
- Abhishesh Homagain
- Faculty of Health, Department of Kinesiology and Health Sciences, University of Waterloo, Waterloo, Ontario, Canada
| | - Kaylena A. Ehgoetz Martens
- Faculty of Health, Department of Kinesiology and Health Sciences, University of Waterloo, Waterloo, Ontario, Canada
| |
Collapse
|
6
|
Wang H, Hu B, Huang J, Chen L, Yuan M, Tian X, Shi T, Zhao J, Huang W. Predicting the fatigue in Parkinson's disease using inertial sensor gait data and clinical characteristics. Front Neurol 2023; 14:1172320. [PMID: 37388552 PMCID: PMC10303817 DOI: 10.3389/fneur.2023.1172320] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2023] [Accepted: 05/23/2023] [Indexed: 07/01/2023] Open
Abstract
Objectives The study aimed to analyze the clinical features and gait characteristics of patients with Parkinson's disease (PD) who also suffer from fatigue and to develop a model that can help identify fatigue states in the early stages of PD. Methodology A total of 81 PD patients have been enrolled for the Parkinson's Fatigue Scale (PFS-16) assessment and divided into two groups: patients with or without fatigue. Neuropsychological assessments of the two groups, including motor and non-motor symptoms, were collected. The patient's gait characteristics were collected using a wearable inertial sensor device. Results PD patients who experienced fatigue had a more significant impairment of motor symptoms than those who did not, and the experience of fatigue became more pronounced as the disease progressed. Patients with fatigue had more significant mood disorders and sleep disturbances, which can lead to a poorer quality of life. PD patients with fatigue had shorter step lengths, lower velocity, and stride length and increased stride length variability. As for kinematic parameters, PD patients with fatigue had lower shank-forward swing max, trunk-max sagittal angular velocity, and lumbar-max coronal angular velocity than PD patients without fatigue. The binary logistic analysis found that Movement Disorder Society-Unified Parkinson's Disease Rating Scale-I (MDS-UPDRS-I) scores, Hamilton Depression Scale (HAMD) scores, and stride length variability independently predicted fatigue in PD patients. The area under the curve (AUC) of these selected factors in the receiver operating characteristic (ROC) analysis was 0.900. Moreover, HAMD might completely mediate the association between Hamilton Anxiety Scale (HAMA) scores and fatigue (indirect effect: β = 0.032, 95% confidence interval: 0.001-0.062), with a percentage of mediation of 55.46%. Conclusion Combining clinical characteristics and gait cycle parameters, including MDS-UPDRS-I scores, HAMD scores, and stride length variability, can identify PD patients with a high fatigue risk.
Collapse
Affiliation(s)
- Hui Wang
- Department of Neurology, The Second Affiliated Hospital of Nanchang University, Nanchang, China
| | - Binbin Hu
- Department of Neurology, The Second Affiliated Hospital of Nanchang University, Nanchang, China
| | - Juan Huang
- Department of Neurology, The Second Affiliated Hospital of Nanchang University, Nanchang, China
| | - Lin Chen
- Department of Neurology, The Second Affiliated Hospital of Nanchang University, Nanchang, China
| | - Min Yuan
- Department of Neurology, Jiangxi Provincial People's Hospital, The First Affiliated Hospital of Nanchang Medical College, Nanchang, China
| | - Xingfu Tian
- Department of Neurology, The Second Affiliated Hospital of Nanchang University, Nanchang, China
| | - Ting Shi
- Department of Neurology, The Second Affiliated Hospital of Nanchang University, Nanchang, China
| | - Jiahao Zhao
- Department of Neurology, The Second Affiliated Hospital of Nanchang University, Nanchang, China
| | - Wei Huang
- Department of Neurology, The Second Affiliated Hospital of Nanchang University, Nanchang, China
| |
Collapse
|
7
|
Khaliluzzaman M, Uddin A, Deb K, Hasan MJ. Person Recognition Based on Deep Gait: A Survey. SENSORS (BASEL, SWITZERLAND) 2023; 23:4875. [PMID: 37430786 DOI: 10.3390/s23104875] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/18/2023] [Revised: 05/12/2023] [Accepted: 05/16/2023] [Indexed: 07/12/2023]
Abstract
Gait recognition, also known as walking pattern recognition, has expressed deep interest in the computer vision and biometrics community due to its potential to identify individuals from a distance. It has attracted increasing attention due to its potential applications and non-invasive nature. Since 2014, deep learning approaches have shown promising results in gait recognition by automatically extracting features. However, recognizing gait accurately is challenging due to the covariate factors, complexity and variability of environments, and human body representations. This paper provides a comprehensive overview of the advancements made in this field along with the challenges and limitations associated with deep learning methods. For that, it initially examines the various gait datasets used in the literature review and analyzes the performance of state-of-the-art techniques. After that, a taxonomy of deep learning methods is presented to characterize and organize the research landscape in this field. Furthermore, the taxonomy highlights the basic limitations of deep learning methods in the context of gait recognition. The paper is concluded by focusing on the present challenges and suggesting several research directions to improve the performance of gait recognition in the future.
Collapse
Affiliation(s)
- Md Khaliluzzaman
- Department of Computer Science and Engineering, Chittagong University of Engineering & Technology, Chattogram 4349, Bangladesh
- Department of Computer Science and Engineering, International Islamic University Chittagong, Chattogram 4318, Bangladesh
| | - Ashraf Uddin
- Department of Computer Science and Engineering, Chittagong University of Engineering & Technology, Chattogram 4349, Bangladesh
| | - Kaushik Deb
- Department of Computer Science and Engineering, Chittagong University of Engineering & Technology, Chattogram 4349, Bangladesh
| | - Md Junayed Hasan
- National Subsea Centre, Robert Gordon University, Aberdeen AB10 7AQ, UK
| |
Collapse
|
8
|
Jahangir F, Khan MA, Alhaisoni M, Alqahtani A, Alsubai S, Sha M, Al Hejaili A, Cha JH. A Fusion-Assisted Multi-Stream Deep Learning and ESO-Controlled Newton-Raphson-Based Feature Selection Approach for Human Gait Recognition. SENSORS (BASEL, SWITZERLAND) 2023; 23:2754. [PMID: 36904963 PMCID: PMC10007680 DOI: 10.3390/s23052754] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/31/2023] [Revised: 02/24/2023] [Accepted: 02/25/2023] [Indexed: 06/18/2023]
Abstract
The performance of human gait recognition (HGR) is affected by the partial obstruction of the human body caused by the limited field of view in video surveillance. The traditional method required the bounding box to recognize human gait in the video sequences accurately; however, it is a challenging and time-consuming approach. Due to important applications, such as biometrics and video surveillance, HGR has improved performance over the last half-decade. Based on the literature, the challenging covariant factors that degrade gait recognition performance include walking while wearing a coat or carrying a bag. This paper proposed a new two-stream deep learning framework for human gait recognition. The first step proposed a contrast enhancement technique based on the local and global filters information fusion. The high-boost operation is finally applied to highlight the human region in a video frame. Data augmentation is performed in the second step to increase the dimension of the preprocessed dataset (CASIA-B). In the third step, two pre-trained deep learning models-MobilenetV2 and ShuffleNet-are fine-tuned and trained on the augmented dataset using deep transfer learning. Features are extracted from the global average pooling layer instead of the fully connected layer. In the fourth step, extracted features of both streams are fused using a serial-based approach and further refined in the fifth step by using an improved equilibrium state optimization-controlled Newton-Raphson (ESOcNR) selection method. The selected features are finally classified using machine learning algorithms for the final classification accuracy. The experimental process was conducted on 8 angles of the CASIA-B dataset and obtained an accuracy of 97.3, 98.6, 97.7, 96.5, 92.9, 93.7, 94.7, and 91.2%, respectively. Comparisons were conducted with state-of-the-art (SOTA) techniques, and showed improved accuracy and reduced computational time.
Collapse
Affiliation(s)
- Faiza Jahangir
- Department of Computer Science, HITEC University, Taxila 47080, Pakistan
| | - Muhammad Attique Khan
- Department of Computer Science, HITEC University, Taxila 47080, Pakistan
- Department of Informatics, University of Leicester, Leicester LE1 7RH, UK
| | - Majed Alhaisoni
- College of Computer Science and Engineering, University of Ha’il, Ha’il 81451, Saudi Arabia
| | - Abdullah Alqahtani
- Department of Software Engineering, College of Computer Engineering and Sciences, Prince Sattam bin Abdulaziz University, Al-kharj 16242, Saudi Arabia
| | - Shtwai Alsubai
- Department of Computer Science, College of Computer Engineering and Sciences, Prince Sattam bin Abdulaziz University, Al-kharj 16242, Saudi Arabia
| | - Mohemmed Sha
- Department of Software Engineering, College of Computer Engineering and Sciences, Prince Sattam bin Abdulaziz University, Al-kharj 16242, Saudi Arabia
| | - Abdullah Al Hejaili
- Faculty of Computers & Information Technology, Computer Science Department, University of Tabuk, Tabuk 71491, Saudi Arabia
| | - Jae-hyuk Cha
- Department of computer Science, Hanyang University, Seoul 04763, Republic of Korea
| |
Collapse
|
9
|
Sepas-Moghaddam A, Etemad A. Deep Gait Recognition: A Survey. IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE 2023; 45:264-284. [PMID: 35167443 DOI: 10.1109/tpami.2022.3151865] [Citation(s) in RCA: 13] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/06/2023]
Abstract
Gait recognition is an appealing biometric modality which aims to identify individuals based on the way they walk. Deep learning has reshaped the research landscape in this area since 2015 through the ability to automatically learn discriminative representations. Gait recognition methods based on deep learning now dominate the state-of-the-art in the field and have fostered real-world applications. In this paper, we present a comprehensive overview of breakthroughs and recent developments in gait recognition with deep learning, and cover broad topics including datasets, test protocols, state-of-the-art solutions, challenges, and future research directions. We first review the commonly used gait datasets along with the principles designed for evaluating them. We then propose a novel taxonomy made up of four separate dimensions namely body representation, temporal representation, feature representation, and neural architecture, to help characterize and organize the research landscape and literature in this area. Following our proposed taxonomy, a comprehensive survey of gait recognition methods using deep learning is presented with discussions on their performances, characteristics, advantages, and limitations. We conclude this survey with a discussion on current challenges and mention a number of promising directions for future research in gait recognition.
Collapse
|
10
|
Zhang Y, Folarin AA, Sun S, Cummins N, Vairavan S, Qian L, Ranjan Y, Rashid Z, Conde P, Stewart C, Laiou P, Sankesara H, Matcham F, White KM, Oetzmann C, Ivan A, Lamers F, Siddi S, Simblett S, Rintala A, Mohr DC, Myin-Germeys I, Wykes T, Haro JM, Penninx BWJH, Narayan VA, Annas P, Hotopf M, Dobson RJB. Associations Between Depression Symptom Severity and Daily-Life Gait Characteristics Derived From Long-Term Acceleration Signals in Real-World Settings: Retrospective Analysis. JMIR Mhealth Uhealth 2022; 10:e40667. [PMID: 36194451 PMCID: PMC9579931 DOI: 10.2196/40667] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2022] [Revised: 08/11/2022] [Accepted: 08/26/2022] [Indexed: 11/19/2022] Open
Abstract
BACKGROUND Gait is an essential manifestation of depression. However, the gait characteristics of daily walking and their relationships with depression have yet to be fully explored. OBJECTIVE The aim of this study was to explore associations between depression symptom severity and daily-life gait characteristics derived from acceleration signals in real-world settings. METHODS We used two ambulatory data sets (N=71 and N=215) with acceleration signals collected by wearable devices and mobile phones, respectively. We extracted 12 daily-life gait features to describe the distribution and variance of gait cadence and force over a long-term period. Spearman coefficients and linear mixed-effects models were used to explore the associations between daily-life gait features and depression symptom severity measured by the 15-item Geriatric Depression Scale (GDS-15) and 8-item Patient Health Questionnaire (PHQ-8) self-reported questionnaires. The likelihood-ratio (LR) test was used to test whether daily-life gait features could provide additional information relative to the laboratory gait features. RESULTS Higher depression symptom severity was significantly associated with lower gait cadence of high-performance walking (segments with faster walking speed) over a long-term period in both data sets. The linear regression model with long-term daily-life gait features (R2=0.30) fitted depression scores significantly better (LR test P=.001) than the model with only laboratory gait features (R2=0.06). CONCLUSIONS This study indicated that the significant links between daily-life walking characteristics and depression symptom severity could be captured by both wearable devices and mobile phones. The daily-life gait patterns could provide additional information for predicting depression symptom severity relative to laboratory walking. These findings may contribute to developing clinical tools to remotely monitor mental health in real-world settings.
Collapse
Affiliation(s)
- Yuezhou Zhang
- Department of Biostatistics & Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
| | - Amos A Folarin
- Department of Biostatistics & Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
- Institute of Health Informatics, University College London, London, United Kingdom
- NIHR Biomedical Research Centre at South London and Maudsley NHS Foundation Trust, London, United Kingdom
- Health Data Research UK London, University College London, London, United Kingdom
- NIHR Biomedical Research Centre at University College London Hospitals, NHS Foundation Trust, London, United Kingdom
| | - Shaoxiong Sun
- Department of Biostatistics & Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
| | - Nicholas Cummins
- Department of Biostatistics & Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
| | | | - Linglong Qian
- Department of Biostatistics & Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
| | - Yatharth Ranjan
- Department of Biostatistics & Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
| | - Zulqarnain Rashid
- Department of Biostatistics & Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
| | - Pauline Conde
- Department of Biostatistics & Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
| | - Callum Stewart
- Department of Biostatistics & Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
| | - Petroula Laiou
- Department of Biostatistics & Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
| | - Heet Sankesara
- Department of Biostatistics & Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
| | - Faith Matcham
- Department of Psychological Medicine, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
- School of Psychology, University of Sussex, Falmer, United Kingdom
| | - Katie M White
- Department of Psychological Medicine, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
| | - Carolin Oetzmann
- Department of Psychological Medicine, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
| | - Alina Ivan
- Department of Psychological Medicine, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
| | - Femke Lamers
- Department of Psychiatry, Amsterdam UMC location Vrije Universiteit, Amsterdam, Netherlands
- Mental Health Program, Amsterdam Public Health Research Institute, Amsterdam, Netherlands
| | - Sara Siddi
- Teaching Research and Innovation Unit, Parc Sanitari Sant Joan de Déu, Fundació Sant Joan de Déu, Barcelona, Spain
- Centro de Investigación Biomédica en Red de Salud Mental, Madrid, Spain
- Faculty of Medicine and Health Sciences, Universitat de Barcelona, Barcelona, Spain
| | - Sara Simblett
- Department of Psychology, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
| | - Aki Rintala
- Department of Neurosciences, Center for Contextual Psychiatry, Katholieke Universiteit Leuven, Leuven, Belgium
- Faculty of Social Services and Health Care, LAB University of Applied Sciences, Lahti, Finland
| | - David C Mohr
- Center for Behavioral Intervention Technologies, Department of Preventive Medicine, Northwestern University, Chicago, IL, United States
| | - Inez Myin-Germeys
- Department of Neurosciences, Center for Contextual Psychiatry, Katholieke Universiteit Leuven, Leuven, Belgium
| | - Til Wykes
- Department of Psychology, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
- South London and Maudsley NHS Foundation Trust, London, United Kingdom
| | - Josep Maria Haro
- Teaching Research and Innovation Unit, Parc Sanitari Sant Joan de Déu, Fundació Sant Joan de Déu, Barcelona, Spain
- Centro de Investigación Biomédica en Red de Salud Mental, Madrid, Spain
- Faculty of Medicine and Health Sciences, Universitat de Barcelona, Barcelona, Spain
| | - Brenda W J H Penninx
- Department of Psychiatry, Amsterdam UMC location Vrije Universiteit, Amsterdam, Netherlands
- Mental Health Program, Amsterdam Public Health Research Institute, Amsterdam, Netherlands
| | | | | | - Matthew Hotopf
- NIHR Biomedical Research Centre at South London and Maudsley NHS Foundation Trust, London, United Kingdom
- Department of Psychological Medicine, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
- South London and Maudsley NHS Foundation Trust, London, United Kingdom
| | - Richard J B Dobson
- Department of Biostatistics & Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
- Institute of Health Informatics, University College London, London, United Kingdom
- NIHR Biomedical Research Centre at South London and Maudsley NHS Foundation Trust, London, United Kingdom
- Health Data Research UK London, University College London, London, United Kingdom
- NIHR Biomedical Research Centre at University College London Hospitals, NHS Foundation Trust, London, United Kingdom
| |
Collapse
|
11
|
Guo Y, Yang J, Liu Y, Chen X, Yang GZ. Detection and assessment of Parkinson's disease based on gait analysis: A survey. Front Aging Neurosci 2022; 14:916971. [PMID: 35992585 PMCID: PMC9382193 DOI: 10.3389/fnagi.2022.916971] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/10/2022] [Accepted: 07/08/2022] [Indexed: 11/13/2022] Open
Abstract
Neurological disorders represent one of the leading causes of disability and mortality in the world. Parkinson's Disease (PD), for example, affecting millions of people worldwide is often manifested as impaired posture and gait. These impairments have been used as a clinical sign for the early detection of PD, as well as an objective index for pervasive monitoring of the PD patients in daily life. This review presents the evidence that demonstrates the relationship between human gait and PD, and illustrates the role of different gait analysis systems based on vision or wearable sensors. It also provides a comprehensive overview of the available automatic recognition systems for the detection and management of PD. The intervening measures for improving gait performance are summarized, in which the smart devices for gait intervention are emphasized. Finally, this review highlights some of the new opportunities in detecting, monitoring, and treating of PD based on gait, which could facilitate the development of objective gait-based biomarkers for personalized support and treatment of PD.
Collapse
Affiliation(s)
- Yao Guo
- Institute of Medical Robotics, Shanghai Jiao Tong University, Shanghai, China
| | - Jianxin Yang
- Institute of Medical Robotics, Shanghai Jiao Tong University, Shanghai, China
| | - Yuxuan Liu
- Institute of Medical Robotics, Shanghai Jiao Tong University, Shanghai, China
| | - Xun Chen
- Department of Electronic Engineering and Information Science, University of Science and Technology of China, Hefei, China
| | - Guang-Zhong Yang
- Institute of Medical Robotics, Shanghai Jiao Tong University, Shanghai, China
| |
Collapse
|
12
|
Presti P, Ruzzon D, Galasso GM, Avanzini P, Caruana F, Vecchiato G. The Avatar's Gist: How to Transfer Affective Components From Dynamic Walking to Static Body Postures. Front Neurosci 2022; 16:842433. [PMID: 35784850 PMCID: PMC9240741 DOI: 10.3389/fnins.2022.842433] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2021] [Accepted: 04/27/2022] [Indexed: 11/30/2022] Open
Abstract
Dynamic virtual representations of the human being can communicate a broad range of affective states through body movements, thus effectively studying emotion perception. However, the possibility of modeling static body postures preserving affective information is still fundamental in a broad spectrum of experimental settings exploring time-locked cognitive processes. We propose a novel automatic method for creating virtual affective body postures starting from kinematics data. Exploiting body features related to postural cues and movement velocity, we transferred the affective components from dynamic walking to static body postures of male and female virtual avatars. Results of two online experiments showed that participants coherently judged different valence and arousal levels in the avatar's body posture, highlighting the reliability of the proposed methodology. In addition, esthetic and postural cues made women more emotionally expressive than men. Overall, we provided a valid methodology to create affective body postures of virtual avatars, which can be used within different virtual scenarios to understand better the way we perceive the affective state of others.
Collapse
Affiliation(s)
- Paolo Presti
- Institute of Neuroscience, National Research Council of Italy, Parma, Italy
- Department of Medicine and Surgery, University of ParmaParma, Italy
| | - Davide Ruzzon
- TUNED, Lombardini22, Milan, Italy
- Dipartimento Culture del Progetto, University IUAV, Venice, Italy
| | | | - Pietro Avanzini
- Institute of Neuroscience, National Research Council of Italy, Parma, Italy
| | - Fausto Caruana
- Institute of Neuroscience, National Research Council of Italy, Parma, Italy
| | - Giovanni Vecchiato
- Institute of Neuroscience, National Research Council of Italy, Parma, Italy
| |
Collapse
|
13
|
Cicirelli G, Impedovo D, Dentamaro V, Marani R, Pirlo G, D'Orazio TR. Human Gait Analysis in Neurodegenerative Diseases: a Review. IEEE J Biomed Health Inform 2021; 26:229-242. [PMID: 34181559 DOI: 10.1109/jbhi.2021.3092875] [Citation(s) in RCA: 28] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
This paper reviews the recent literature on technologies and methodologies for quantitative human gait analysis in the context of neurodegnerative diseases. The use of technological instruments can be of great support in both clinical diagnosis and severity assessment of these pathologies. In this paper, sensors, features and processing methodologies have been reviewed in order to provide a highly consistent work that explores the issues related to gait analysis. First, the phases of the human gait cycle are briefly explained, along with some non-normal gait patterns (gait abnormalities) typical of some neurodegenerative diseases. The work continues with a survey on the publicly available datasets principally used for comparing results. Then the paper reports the most common processing techniques for both feature selection and extraction and for classification and clustering. Finally, a conclusive discussion on current open problems and future directions is outlined.
Collapse
|
14
|
Doki S, Sasahara S, Hori D, Oi Y, Takahashi T, Shiraki N, Ikeda Y, Ikeda T, Arai Y, Muroi K, Matsuzaki I. Comparison of predicted psychological distress among workers between artificial intelligence and psychiatrists: a cross-sectional study in Tsukuba Science City, Japan. BMJ Open 2021; 11:e046265. [PMID: 34162646 PMCID: PMC8231007 DOI: 10.1136/bmjopen-2020-046265] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/08/2023] Open
Abstract
OBJECTIVES Psychological distress is a worldwide problem and a serious problem that needs to be addressed in the field of occupational health. This study aimed to use artificial intelligence (AI) to predict psychological distress among workers using sociodemographic, lifestyle and sleep factors, not subjective information such as mood and emotion, and to examine the performance of the AI models through a comparison with psychiatrists. DESIGN Cross-sectional study. SETTING We conducted a survey on psychological distress and living conditions among workers. An AI model for predicting psychological distress was created and then the results were compared in terms of accuracy with predictions made by psychiatrists. PARTICIPANTS An AI model of the neural network and six psychiatrists. PRIMARY OUTCOME The accuracies of the AI model and psychiatrists for predicting psychological distress. METHODS In total, data from 7251 workers were analysed to predict moderate and severe psychological distress. An AI model of the neural network was created and accuracy, sensitivity and specificity were calculated. Six psychiatrists used the same data as the AI model to predict psychological distress and conduct a comparison with the AI model. RESULTS The accuracies of the AI model and psychiatrists for predicting moderate psychological distress were 65.2% and 64.4%, respectively, showing no significant difference. The accuracies of the AI model and psychiatrists for predicting severe psychological distress were 89.9% and 85.5%, respectively, indicating that the AI model had significantly higher accuracy. CONCLUSIONS A machine learning model was successfully developed to screen workers with depressed mood. The explanatory variables used for the predictions did not directly ask about mood. Therefore, this newly developed model appears to be able to predict psychological distress among workers easily, regardless of their subjective views.
Collapse
Affiliation(s)
- Shotaro Doki
- Faculty of medicine, University of Tsukuba, Tsukuba, Japan
| | | | - Daisuke Hori
- Faculty of medicine, University of Tsukuba, Tsukuba, Japan
| | - Yuichi Oi
- Faculty of medicine, University of Tsukuba, Tsukuba, Japan
| | - Tsukasa Takahashi
- Graduate School of Comprehensive Human Sciences, University of Tsukuba, Tsukuba, Japan
| | - Nagisa Shiraki
- Graduate School of Comprehensive Human Sciences, University of Tsukuba, Tsukuba, Japan
| | - Yu Ikeda
- Graduate School of Comprehensive Human Sciences, University of Tsukuba, Tsukuba, Japan
| | - Tomohiko Ikeda
- Graduate School of Comprehensive Human Sciences, University of Tsukuba, Tsukuba, Japan
| | - Yo Arai
- Graduate School of Comprehensive Human Sciences, University of Tsukuba, Tsukuba, Japan
| | - Kei Muroi
- Graduate School of Comprehensive Human Sciences, University of Tsukuba, Tsukuba, Japan
| | - Ichiyo Matsuzaki
- Faculty of medicine, University of Tsukuba, Tsukuba, Japan
- International Institute for Integrative Sleep Medicine, University of Tsukuba, Tsukuba, Japan
| |
Collapse
|
15
|
Gao M, Sun H, Cheng X, Gao D, Qiao M. Magnetic resonance imaging in mood disorders: a bibliometric analysis from 1999 to 2020. Clin Transl Imaging 2021. [DOI: 10.1007/s40336-021-00425-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
Abstract
Abstract
Objective
Globally, mood disorders are highly prevalent, and are associated with increased morbidity and mortalities. Magnetic resonance imaging is widely used in the study of mood disorders. However, bibliometric analyses of the state of this field are lacking.
Methods
A literature search in the web of science core collection (WoSCC) for the period between 1945 and 2020 returned 3073 results. Data extracted from these publications include, publication year, journal names, countries of origin, institutions, author names and research areas. The bibliometric method, CiteSpace V and key words analysis were used to visualize the collaboration network and identify research trends, respectively.
Results
Since it was first reported in 1999, the use of magnetic resonance imaging in studies on mood disorders has been increasing. Biological psychiatry is the core journal that has extensively published on this topic, while the UNIV PITTSBURGH, USA, has the highest published papers on this topic. Keyword analysis indicated that studies on depression, bipolar disorders, and schizophrenia, with a focus on specific brain regions, including amygdala, prefrontal cortex and anterior cingulate cortex are key research topics.
Conclusion
Brain structure and network, sex differences, and treatment-associated brain changes are key topics of future research.
Collapse
|
16
|
Gu X, Guo Y, Deligianni F, Lo B, Yang GZ. Cross-Subject and Cross-Modal Transfer for Generalized Abnormal Gait Pattern Recognition. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2021; 32:546-560. [PMID: 32726285 DOI: 10.1109/tnnls.2020.3009448] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/11/2023]
Abstract
For abnormal gait recognition, pattern-specific features indicating abnormalities are interleaved with the subject-specific differences representing biometric traits. Deep representations are, therefore, prone to overfitting, and the models derived cannot generalize well to new subjects. Furthermore, there is limited availability of abnormal gait data obtained from precise Motion Capture (Mocap) systems because of regulatory issues and slow adaptation of new technologies in health care. On the other hand, data captured from markerless vision sensors or wearable sensors can be obtained in home environments, but noises from such devices may prevent the effective extraction of relevant features. To address these challenges, we propose a cascade of deep architectures that can encode cross-modal and cross-subject transfer for abnormal gait recognition. Cross-modal transfer maps noisy data obtained from RGBD and wearable sensors to accurate 4-D representations of the lower limb and joints obtained from the Mocap system. Subsequently, cross-subject transfer allows disentangling subject-specific from abnormal pattern-specific gait features based on a multiencoder autoencoder architecture. To validate the proposed methodology, we obtained multimodal gait data based on a multicamera motion capture system along with synchronized recordings of electromyography (EMG) data and 4-D skeleton data extracted from a single RGBD camera. Classification accuracy was improved significantly in both Mocap and noisy modalities.
Collapse
|
17
|
Ruet A, Biau S, Arnould C, Galloux P, Destrez A, Pycik E, Boichot L, Lansade L. Horses Could Perceive Riding Differently Depending on the Way They Express Poor Welfare in the Stable. J Equine Vet Sci 2020; 94:103206. [PMID: 33077101 DOI: 10.1016/j.jevs.2020.103206] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2020] [Revised: 06/21/2020] [Accepted: 07/20/2020] [Indexed: 01/09/2023]
Abstract
This study investigated the relationships between four behavioral and postural indicators of a compromised welfare state in loose boxes (stereotypies, aggressive behaviors toward humans, withdrawn posture reflecting unresponsiveness to the environment, and alert posture indicating hypervigilance) and the way horses perceived riding. This perception was inferred using a survey completed by the usual riding instructor and during a standardized riding session (assessment of behaviors and postures, qualitative behavior assessment (QBA) and characterization of the horses' locomotion using an inertial measurement unit). In accordance with ear and tail positions and the QBA, stereotypic and the most hypervigilant horses in loose boxes seemed to experience a more negative affective state during the riding session compared with nonstereotypic and less hypervigilant animals (P < .02 in all cases). Horses which were aggressive toward humans in loose boxes had higher scores regarding the occurrence of discomfort and defensive behaviors on the survey than nonaggressive horses (P = .03). They also presented higher dorsoventral accelerations at a canter during the riding session (P = .03), requiring the rider to increase his spinal movement (P = .005). These results suggest that aggressive horses may be harder to ride than nonaggressive animals. The expression of unresponsiveness to the environment in loose boxes was related to more reluctance to move forward, as assessed in the survey (P = .006). This study suggests that a compromised welfare state in the stable is related to horses having a more negative perception of riding. This perception could vary depending on the expression of poor welfare.
Collapse
Affiliation(s)
- Alice Ruet
- UMR 0085 INRAE, PRC, CNRS, UMR 7247, IFCE, Université de Tours, Nouzilly, France.
| | - Sophie Biau
- I.F.C.E. Ecole Nationale d'Equitation, Terrefort, BP 207, 49411, Saumur, France
| | - Cécile Arnould
- UMR 0085 INRAE, PRC, CNRS, UMR 7247, IFCE, Université de Tours, Nouzilly, France
| | - Patrick Galloux
- I.F.C.E. Ecole Nationale d'Equitation, Terrefort, BP 207, 49411, Saumur, France
| | - Alexandra Destrez
- AgroSup Dijon, Université de Bourgogne Franche-Comté, INRAE, CNRS, UMR6265 CSGA - Centre des Sciences du Goût et de l'Alimentation, Dijon, France
| | - Eléna Pycik
- I.F.C.E. Ecole Nationale d'Equitation, Terrefort, BP 207, 49411, Saumur, France
| | - Laetitia Boichot
- I.F.C.E. Ecole Nationale d'Equitation, Terrefort, BP 207, 49411, Saumur, France
| | - Léa Lansade
- UMR 0085 INRAE, PRC, CNRS, UMR 7247, IFCE, Université de Tours, Nouzilly, France
| |
Collapse
|