Embodiment of Sadness and Depression—Gait Patterns Associated With Dysphoric Mood

Michalak, Johannes; Troje, Nikolaus F.; Fischer, Julia; Vollmar, Patrick; Heidenreich, Thomas; Schulte, Dietmar

doi:10.1097/psy.0b013e3181a2515c

Cited by 377 publications

(318 citation statements)

References 26 publications

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“…In particular, individuals with depression walk slowly with a shorter stride length, longer standing phase, and increased gait variability. 15,16 These gait patterns characteristic of depression have been shown to be associated with falls. 17,18 The complex relationship between depression, cognitive performance, motor performance, and risk of falls is illustrated by a recent study that found that the association between depression and slowed choice stepping reaction time (which is a composite measure of fall risk) was mediated by two paths: one through quadriceps strength and the other through executive function, with both mediating variables then influencing choice stepping reaction time via simple reaction time and balance.…”

Section: Depression and Fallsmentioning

confidence: 99%

The Complex Interplay of Depression and Falls in Older Adults: A Clinical Review

Iaboni

Flint

2013

The American Journal of Geriatric Psychiatry

225

178

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Depression and falls have a significant bidirectional relationship. Excessive fear of falling, which is frequently associated with depression, also increases the risk of falls. Both depression and fear of falling are associated with impairment of gait and balance, an association that is mediated through cognitive, sensory, and motor pathways. The management of depression in fall-prone individuals is challenging, since antidepressant medications can increase the risk of falls, selective serotonin reuptake inhibitors may increase the risk of fragility fractures, and data are lacking about the effect of fall rehabilitation programs on clinically significant depression. Based on the current state of knowledge, exercise (particularly Tai Chi) and cognitive-behavioral therapy should be considered for the first-line treatment of mild depression in older fallers. Antidepressant medications are indicated to treat moderate to severe depression in fall-prone individuals, but with appropriate precautions including low starting dose and slow dose titration, use of psychotropic monotherapy whenever possible, and monitoring for orthostatic hypotension and hyponatremia. To date, there have been no recommendations for osteoporosis monitoring and treatment in individuals prescribed antidepressant medications, beyond the usual clinical guidelines. However, treatment of the older depressed person who is at risk of falls provides the opportunity to inquire about his or her adherence with osteoporosis and fracture-prevention guidelines. KeywordsAntidepressants; depression; falls; fear of falling; fractures One-third of community-dwelling seniors fall each year, with half of these individuals falling more than once. Ten percent of all falls result in a major injury, which can lead to institutionalization and death. 1 Falls often result in a fear of further falls, leading to fearrelated restriction of activities: this activity restriction has been associated with subsequent impairment in balance, mobility, and disability. 2,3 Send correspondence and reprint requests to Alastair

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Section: Depression and Fallsmentioning

confidence: 99%

The Complex Interplay of Depression and Falls in Older Adults: A Clinical Review

Iaboni

Flint

2013

The American Journal of Geriatric Psychiatry

225

178

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“…Our emotional state is often expressed in a variety of means, such as face, voice, body posture, and walking gait (1,2). Many studies are conducted in strict laboratory settings, which may impede the overall ecological validity of the findings.…”

Section: Introductionmentioning

confidence: 99%

Emotion Recognition Using Smart Watch Sensor Data: A Mixed-Design Study (Preprint)

Quiroz¹,

Geangu²,

Yong³

2018

Preprint

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Background: Research in psychology has shown that the way a person walks reflects that person's current mood (or emotional state). Recent studies have started using smartphones to detect emotional states from movement data. Objective: This study investigates the use of movement sensor data from a smart watch to infer an individual's emotional state. We present our findings on a user study with 50 participants. Methods: The experimental design is a mixed-design study; within-subjects (emotions; happy, sad, neutral) and between-subjects (stimulus type: audio visual "movie clips", audio "music clips"). Each participant experienced both emotions in a single stimulus type. All participants walked 250m while wearing a smart watch on one wrist and a heart rate monitor strap on their chest. They also had to answer a short questionnaire (20 items; PANAS) before and after experiencing each emotion. The heart rate monitor serves as a supplementary information to our data. We performed time-series analysis on the data from the smart watch and a t-test on the questionnaire items to measure the change in emotional state. The heart rate data was analyzed using one-way ANOVA. We extracted features from the time-series using sliding windows and used the features to train and validate classifiers that determine an individual's emotion. Results: We had 50 young adults participate in our study, with 49 included for the affective PANAS questionnaire and all for the feature extraction. Participants reported feeling less negative affect after watching sad videos or after listening to the sad music, P < .006. For the task of emotion recognition using classifiers, our results show that the personal models outperformed personal baselines, and achieve median accuracies higher than 78% for all conditions of the design study for the binary classification of happiness vs sadness. Conclusions: Our findings show that we are able to detect the changes in emotional state with data obtained from the smartwatch as well as behavioral responses. Together with the high accuracies achieved across all users for the classification of happy vs sad emotional states, this is further evidence for the hypothesis that movement sensor data can be used for emotion recognition.

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“…A useful source of information for assisting an owner is his/her gait (walking) signal, because the human gait conveys a variety of information such as personality [13], gender [14], physical or clinical condition [15], [16], and mood [17], and can easily be captured by accelerometers. In particular, personality in gait signal plays quite an important role when recognizing the person carrying the device.…”

Section: Introductionmentioning

confidence: 99%

Orientation-Compensative Signal Registration for Owner Authentication Using an Accelerometer

Ngo

Makihara

Nagahara

et al. 2014

IEICE Trans. Inf. & Syst.

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SUMMARYGait-based owner authentication using accelerometers has recently been extensively studied owing to the development of wearable electronic devices. An actual gait signal is always subject to change due to many factors including variation of sensor attachment. In this research, we tackle to the practical sensor-orientation inconsistency, for which signal sequences are captured at different sensor orientations. We present an iterative signal matching algorithm based on phase-registration technique to simultaneously estimate relative sensor-orientation and register the 3D acceleration signals. The iterative framework is initialized by using 1D orientation-invariant resultant signals which are computed from 3D signals. As a result, the matching algorithm is robust to any initial sensororientation. This matching algorithm is used to match a probe and a gallery signals in the proposed owner authentication method. Experiments using actual gait signals under various conditions such as different days, sensors, weights being carried, and sensor orientations show that our authentication method achieves positive results.

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Embodiment of Sadness and Depression—Gait Patterns Associated With Dysphoric Mood

Cited by 377 publications

References 26 publications

The Complex Interplay of Depression and Falls in Older Adults: A Clinical Review

The Complex Interplay of Depression and Falls in Older Adults: A Clinical Review

Emotion Recognition Using Smart Watch Sensor Data: A Mixed-Design Study (Preprint)

Orientation-Compensative Signal Registration for Owner Authentication Using an Accelerometer

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