Privacy implications of accelerometer data: a review of possible inferences <s> Mood and Emotion Recognition <s> The positive effects of physical activity on mood are well documented in cross-sectional studies. To date there have been only a few studies analyzing within-subject covariance between physical activity and mood in everyday life. This study aims to close this gap using an ambulatory assessment of mood and physical activity. Thirteen participants completed a standardized diary over a 10-week period, resulting in 1,860 measurement points. Valence, energetic arousal, and calmness are the three subscales of mood that were assessed. Participants rated their mood promptly after self-selected activities. A multilevel analysis indicates that the three dimensions of mood were positively affected by episodes of physical activity, such as walking or gardening—valence: t(12) = 5.6, p < .001; energetic arousal: t(12) = 2.4, p = .033; calmness: t(12) = 2.8, p = .015. Moreover, the association is affected by the individual baseline mood level, with the greatest effect seen when mood is depressed. <s> BIB001 </s> Privacy implications of accelerometer data: a review of possible inferences <s> Mood and Emotion Recognition <s> Increase in workload across many organizations and consequent increase in occupational stress are negatively affecting the health of the workforce. Measuring stress and other human psychological dynamics is difficult due to subjective nature of selfreporting and variability between and within individuals. With the advent of smartphones, it is now possible to monitor diverse aspects of human behavior, including objectively measured behavior related to psychological state and consequently stress. We have used data from the smartphone's built-in accelerometer to detect behavior that correlates with subjects stress levels. Accelerometer sensor was chosen because it raises fewer privacy concerns (e.g., in comparison to location, video, or audio recording), and because its low-power consumption makes it suitable to be embedded in smaller wearable devices, such as fitness trackers. About 30 subjects from two different organizations were provided with smartphones. The study lasted for eight weeks and was conducted in real working environments, with no constraints whatsoever placed upon smartphone usage. The subjects reported their perceived stress levels three times during their working hours. Using combination of statistical models to classify selfreported stress levels, we achieved a maximum overall accuracy of 71% for user-specific models and an accuracy of 60% for the use of similar-users models, relying solely on data from a single accelerometer. <s> BIB002 </s> Privacy implications of accelerometer data: a review of possible inferences <s> Mood and Emotion Recognition <s> BACKGROUND ::: Recently, emotion recognition has become a hot topic in human-computer interaction. If computers could understand human emotions, they could interact better with their users. This paper proposes a novel method to recognize human emotions (neutral, happy, and angry) using a smart bracelet with built-in accelerometer. ::: ::: ::: METHODS ::: In this study, a total of 123 participants were instructed to wear a customized smart bracelet with built-in accelerometer that can track and record their movements. Firstly, participants walked two minutes as normal, which served as walking behaviors in a neutral emotion condition. Participants then watched emotional film clips to elicit emotions (happy and angry). The time interval between watching two clips was more than four hours. After watching film clips, they walked for one minute, which served as walking behaviors in a happy or angry emotion condition. We collected raw data from the bracelet and extracted a few features from raw data. Based on these features, we built classification models for classifying three types of emotions (neutral, happy, and angry). ::: ::: ::: RESULTS AND DISCUSSION ::: For two-category classification, the classification accuracy can reach 91.3% (neutral vs. angry), 88.5% (neutral vs. happy), and 88.5% (happy vs. angry), respectively; while, for the differentiation among three types of emotions (neutral, happy, and angry), the accuracy can reach 81.2%. ::: ::: ::: CONCLUSIONS ::: Using wearable devices, we found it is possible to recognize human emotions (neutral, happy, and angry) with fair accuracy. Results of this study may be useful to improve the performance of human-computer interaction. <s> BIB003 </s> Privacy implications of accelerometer data: a review of possible inferences <s> Mood and Emotion Recognition <s> The paper outlines work on the classification of emotions using smartphone accelerometer data. Such classification can be used, in conjunction with other methods of emotion detection, to adapt services to the user's emotional state. The data is collected from individuals who have been carrying their phone in a pocket while walking. An Android app was developed in order to monitor the smartphone accelerometer of the individuals who participated in the study and occasionally requested them to judge and submit their emotional state. This way, data is collected from a natural environment rather than a laboratory setting. The recorded data is then processed and used to train different classifiers to be compared. The machine learning algorithms decision tree, support vector machine and multilayer perceptron are used for this purpose. Emotions are classified in two dimensions: pleasantness and arousal (activation). While the recognition rate for the arousal dimension is promising at 75%, pleasantness is harder to predict, with a recognition rate of 51%. These findings indicate that by only analyzing accelerometer data recorded from a smartphone, it is possible to make predictions of a person's state of activation. <s> BIB004
The level of physical activity, which can be measured using bodyworn accelerometers (see section 2.1), has been identified as a potential predictor of human emotions BIB001 and depressive moods  . Zhang et al. were able to recognize emotional states of test subjects (happy, neutral, and angry) with fair accuracy, relying only on accelerometer data from smart wristbands BIB003 . Accelerometers in smartphones have been used to detect stress levels BIB002 and arousal BIB004 in users. Also, Matic et al. found a positive association between accelerometer-derived speech activity and mood changes  .