Relationship between personality and gait: predicting personality with gait features

Sun, Jiumo; Wu, Peiqi; Shen, Yulong; Yang, Zhijun; Li, Hualiang; Liu, Yuzhong; Zhu, Tingshao; Li, Li; Zhang, Kai; Chen, Min

doi:10.1109/bibm.2018.8621300

Cited by 16 publications

(16 citation statements)

References 16 publications

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“…Satchell et al analyzed gait using a motion capture system and a treadmill and reported that several traits of the Big Five were correlated with a range of body motions [19]. Sun et al tracked 25 joints of the body with Kinect while walking on a short carpet and maintained that some gait features had strong correlations with [21]. Gait features are indeed attractive because it is situation-independent and easily applicable to various business domains.…”

Section: Related Workmentioning

confidence: 99%

“…One of the few cues available as (a) customer data for offline customers is the customer's visually observable features, such as physical appearance and behaviors ("visual data"). Although much work has been done on personality prediction with the visual data, most studies cannot be applied to face-to-face services for three reasons: they (1) utilized features of situation-dependent behaviors (e.g., group interactions in a party [25] and daily routine activities at home [6]), (2) collected data with some special devices not installed in common stores (e.g., motion capture systems [19]), and (3) assumed an experimental environment with few occlusions [6,21,25]. Moreover, even if the visual data is collected, it is difficult to collect the corresponding (b) ground truth compared to online customers, whom the company can ask a questionnaire online.…”

Section: Introductionmentioning

confidence: 99%

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Multimodal Self-Assessed Personality Prediction in the Wild

Kamisaka

Ishikawa

2020

Companion Publication of the 2020 International Conference on Multimodal Interaction

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Multimodal Self-Assessed Personality Prediction in the Wild

Kamisaka

Ishikawa

2020

Companion Publication of the 2020 International Conference on Multimodal Interaction

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“…The fascia system can become densified around our physiological asymmetries, such as one leg being slightly shorter than the other, during repetitive movement patterns in modern lifestyles [ 84 , 85 ]. This can shape how we walk (i.e., our gait), which can affect what we remember [ 86 ] and influence our personalities [ 87 ]. This is an example of mind–body behaviors involving complex psychomotor interactions, rather than being wholly brain-based.…”

Section: Critical Realist Framing Of Psychomotor Experiencementioning

confidence: 99%

Psychomotor Predictive Processing

Fox

2021

Entropy

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Psychomotor experience can be based on what people predict they will experience, rather than on sensory inputs. It has been argued that disconnects between human experience and sensory inputs can be addressed better through further development of predictive processing theory. In this paper, the scope of predictive processing theory is extended through three developments. First, by going beyond previous studies that have encompassed embodied cognition but have not addressed some fundamental aspects of psychomotor functioning. Second, by proposing a scientific basis for explaining predictive processing that spans objective neuroscience and subjective experience. Third, by providing an explanation of predictive processing that can be incorporated into the planning and operation of systems involving robots and other new technologies. This is necessary because such systems are becoming increasingly common and move us farther away from the hunter-gatherer lifestyles within which our psychomotor functioning evolved. For example, beliefs that workplace robots are threatening can generate anxiety, while wearing hardware, such as augmented reality headsets and exoskeletons, can impede the natural functioning of psychomotor systems. The primary contribution of the paper is the introduction of a new formulation of hierarchical predictive processing that is focused on psychomotor functioning.

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“…Unimodal approaches with visual data focused on physical and behavioral features. Examples of such features were the distance from the closest person and head pose [32], upper-body movements [9], and gait features [25,29]. However, most studies were conducted under laboratory environments using special sensors (e.g., pan tilt zoom cameras and a motion capture system) that are not installed in ordinary stores.…”

Section: Related Workmentioning

confidence: 99%

“…Thus, it is not realistic to deploy these approaches in real-world offline services. Moreover, they could predict only one or two traits of the Big Five [29,32].…”

Section: Related Workmentioning

confidence: 99%

Personality Prediction with Cross-Modality Feature Projection

Kamisaka

Ishikawa

2021

Proceedings of the 2021 International Conference on Multimodal Interaction

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In this paper, we propose an approach to predict customers' personalities leveraging two modalities of customers' data: (a) service usages logs of online services and (b) visual data collected in physical stores by surveillance cameras (e.g., gait and whereabouts). A number of companies provide services via online and offline nowadays, thus need to serve two different kinds of customers: "online customers, " who use services completely online and have only (a); and "offline customers, " who use only physical stores and have only (b). To improve personality prediction accuracy for these customers, our approach generates pseudo features of a non-existent modality from the other modality that the customers actually have (i.e., feature projection; e.g., generate pseudo visual data of the online customers from their real online service logs), and uses both pseudo and real features to predict their personalities. The evaluation using real-world data of a mobile carrier's customers showed that our approach predicted personality more accurately than an ordinary unimodal approach for both online and offline customers. We also examined importance of the feature projection and compared two different projection methods. CCS CONCEPTS• Computing methodologies → Machine learning; • Humancentered computing → Human computer interaction (HCI).

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Relationship between personality and gait: predicting personality with gait features

Cited by 16 publications

References 16 publications

Multimodal Self-Assessed Personality Prediction in the Wild

Multimodal Self-Assessed Personality Prediction in the Wild

Psychomotor Predictive Processing

Personality Prediction with Cross-Modality Feature Projection

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