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Peter Gloor

Massachusetts Institute of Technology (MIT), Center for Collective Intelligence, Department Member

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I am happily juggling different hats, studying Collaborative Innovation Networks at MIT's Center for Collective Intelligence, developing the social networking and data visualization software Condor (formerly called TeCFlow) as Chief Creative Officer of startup galaxyadvisors, and occasionally writing books, most recently "Coolfarming - Turn Your Great Idea into the next Big Thing", "Coolhunting - Chasing Down the Next Big Thing" (jointly with Scott Cooper) and "Swarm Creativity – Competitive Advantage through Collaborative Innovation Networks".
Phone: +1 617 253 7018

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Papers by Peter Gloor

Recognizing Individuals and Their Emotions Using Plants as Bio-Sensors through Electro-static Discharge

arXiv (Cornell University), May 10, 2020

By measuring the electrostatic discharge of human bodies together with Mimosa Pudica and other pl... more By measuring the electrostatic discharge of human bodies together with Mimosa Pudica and other plants in response to the human movement we have been able to recognize (a) individuals based on their distinctive pattern of body movements with 66% accuracy as well as (b) positive or negative mood based on their gait characteristics with 85% accuracy. We use the Plant SpikerBox, a device that measures the electrical action potential while also measuring the electrostatic discharge between the electrode on the leaves of a plant and the capacitively coupled human body.

Analyzing VC Influence on Startup Success: A People-Centric Network Theory Approach

Studies on entrepreneurship, structural change and industrial dynamics, 2018

In this thesis, we study the impact of venture capitalists on startup success using social networ... more In this thesis, we study the impact of venture capitalists on startup success using social network analysis. Using multiple sources, we compile a unique dataset of 3199 US-based technology startups and their board members, from which we generate and analyze the interlocking directorates network (formal network) and Twitter network (informal network). We find that startups with more VC board members are more central in the formal network, receive greater funding, have greater annual sales, yet a smaller return-on-investment. We also find that VCs are more central in the Twitter network than non-VCs, have greater Twitter popularity, yet tweet significantly less. Our results indicate that VCs carry a considerable amount of financial and social capital, which they transmit to the startups they invest in, yet their participation leads to lower startup ROI. Additionally, our dataset enabled us to investigate more general questions regarding startup success, including gender diversity on startup boards.

Can Plants Sense Humans? - Using Plants as Biosensors to Detect the Presence of Eurythmic Gestures

This paper describes preliminary results of measuring the impact of human body movements on plant... more This paper describes preliminary results of measuring the impact of human body movements on plants. In particular, we analyze the influence of eurythmic gestures of human actors on lettuce and beans. In an eight week experiment, we exposed rows of lettuce and beans to weekly eurythmic movements (similar to Qi Gong) of a eurythmist, while at the same time measuring changes in voltage between the roots and leaves of lettuce and beans using the plant spikerbox (https://backyardbrains.com/products/plantspikerbox). We compared this experimental group of vegetables with a control group of vegetables whose voltage differential was also measured while never being exposed to eurythmy. Using t-tests, we found a clear difference between the experimental and the control group which was also verified with a machine learning model. In other words, the vegetables showed a noticeably different pattern in electric potentials in response to eurythmic gestures.

More Active Internet-Search on Google and Twitter Posting for COVID-19 Corresponds with Lower Infection Rate in the 50 U.S. States

As the novel coronavirus disease 2019 (COVID-19) continues to rage worldwide, the United States h... more As the novel coronavirus disease 2019 (COVID-19) continues to rage worldwide, the United States has become the most affected country with more than 2.5 million total confirmed cases up to now (June 2, 2020). In this work, we investigate the predictive power of online social media and Internet search for the COVID-19 pandemic among 50 U.S. states. By collecting the state-level daily trends through both Twitter and Google Trends, we observe a high but state-different lag correlation with the number of daily confirmed cases. We further find that the predictive accuracy measured by the correlation coefficient is positively correlated to a state’s demographic, air traffic volume and GDP development. Most importantly, we show that a state’s early infection rate is negatively correlated with the lag to the previous peak in Internet search and tweeting about COVID-19, indicating that the earlier the collective awareness on Twitter/Google in a state, the lower is the infection rate.

Making you happy makes me happy" -- Measuring Individual Mood with Smartwatches

arXiv (Cornell University), Nov 13, 2017

What Makes a Message Persuasive? Identifying Adaptations Towards Persuasiveness in Nine Exploratory Case Studies

arXiv (Cornell University), Apr 26, 2021

The ability to persuade others is critical to professional and personal success. However, craftin... more The ability to persuade others is critical to professional and personal success. However, crafting persuasive messages is demanding and poses various challenges. We conducted nine exploratory case studies to identify adaptations that professional and non-professional writers make in written scenarios to increase their subjective persuasiveness. Furthermore, we identified challenges that those writers faced and identified strategies to resolve them with persuasive natural language generation, i.e., artificial intelligence. Our findings show that humans can achieve high degrees of persuasiveness (more so for professional-level writers), and artificial intelligence can complement them to achieve increased celerity and alignment in the process.

Measuring Ethical Values with AI for Better Teamwork

Future Internet, Apr 27, 2022

Do employees with high ethical and moral values perform better? Comparing personality characteris... more Do employees with high ethical and moral values perform better? Comparing personality characteristics, moral values, and risk-taking behavior with individual and team performance has long been researched. Until now, these determinants of individual personality have been measured through surveys. However, individuals are notoriously bad at self-assessment. Combining machine learning (ML) with social network analysis (SNA) and natural language processing (NLP), this research draws on email conversations to predict the personal values of individuals. These values are then compared with the individual and team performance of employees. This prediction builds on a two-layered ML model. Building on features of social network structure, network dynamics, and network content derived from email conversations, we predict personality characteristics, moral values, and the risk-taking behavior of employees. In turn, we use these values to predict individual and team performance. Our results indicate that more conscientious and less extroverted team members increase the performance of their teams. Willingness to take social risks decreases the performance of innovation teams in a healthcare environment. Similarly, a focus on values such as power and self-enhancement increases the team performance of a global services provider. In sum, the contributions of this paper are twofold: it first introduces a novel approach to measuring personal values based on "honest signals" in emails. Second, these values are then used to build better teams by identifying ideal personality characteristics for a chosen task.

Assessing the Predictive Power of Online Social Media to Analyze COVID-19 Outbreaks in the 50 U.S. States

Future Internet

As the coronavirus disease 2019 (COVID-19) continues to rage worldwide, the United States has bec... more As the coronavirus disease 2019 (COVID-19) continues to rage worldwide, the United States has become the most affected country, with more than 34.1 million total confirmed cases up to 1 June 2021. In this work, we investigate correlations between online social media and Internet search for the COVID-19 pandemic among 50 U.S. states. By collecting the state-level daily trends through both Twitter and Google Trends, we observe a high but state-different lag correlation with the number of daily confirmed cases. We further find that the accuracy measured by the correlation coefficient is positively correlated to a state’s demographic, air traffic volume and GDP development. Most importantly, we show that a state’s early infection rate is negatively correlated with the lag to the previous peak in Internet searches and tweeting about COVID-19, indicating that earlier collective awareness on Twitter/Google correlates with a lower infection rate. Lastly, we demonstrate that correlations bet...

Recognizing Individuals and Their Emotions Using Plants as Bio-Sensors through Electro-static Discharge

arXiv (Cornell University), May 10, 2020

By measuring the electrostatic discharge of human bodies together with Mimosa Pudica and other pl... more By measuring the electrostatic discharge of human bodies together with Mimosa Pudica and other plants in response to the human movement we have been able to recognize (a) individuals based on their distinctive pattern of body movements with 66% accuracy as well as (b) positive or negative mood based on their gait characteristics with 85% accuracy. We use the Plant SpikerBox, a device that measures the electrical action potential while also measuring the electrostatic discharge between the electrode on the leaves of a plant and the capacitively coupled human body.

Wuity as Higher Cognition Combining Intuitive and Deliberate Judgments for Creativity: Analyzing Elon Musk’s Way to Innovate

Studies on Entrepreneurship, Structural Change and Industrial Dynamics, 2018

Derived from unifying dual-process theories of cognitive psychology, we propose a new concept of ... more Derived from unifying dual-process theories of cognitive psychology, we propose a new concept of deliberate intuition as a construct of higher-level cognition that integrates intuitive and deliberate judgments. We introduce "Wuity" cognition embedded into Chinese philosophy, medicine and practices, which is defined by the capability of deliberate intuition and intuitive insights, based on imagery reasoning and non-dualistic thinking and manifested as mindful observation and visual analogy. We develop a framework of three features of Wuity and six steps of Wuity-based innovation. As a case study illustrating our method, we analyze Elon Musk"s way to innovate and discuss common aspects of Wuity as a higher cognition and creative thinking way of innovators in East and West.

Measuring Workload and Performance of Surgeons Using Body Sensors of Smartwatches

Digital Transformation of Collaboration, 2020

We present the first steps toward building an intelligent system to measure the workload and surg... more We present the first steps toward building an intelligent system to measure the workload and surgical performance of minimally invasive surgeons. This pilot study was conducted during two training courses in minimally invasive suturing, one in microsurgery and one in laparoscopic surgery. During each training activity, surgeons wore a smartwatch with the happimeter application running on it. This system recorded a set of physiological and motion parameters during the surgical execution. We found that monitoring the surgeon's maneuvers and physiological parameters during surgical activity has the potential to play an important role in predicting the workload and surgical performance, especially regarding physical and mental demand and the level of distraction during surgery.

When Old Meets New: Emotion Recognition from Speech Signals

Cognitive Computation, 2021

Speech is one of the most natural communication channels for expressing human emotions. Therefore... more Speech is one of the most natural communication channels for expressing human emotions. Therefore, speech emotion recognition (SER) has been an active area of research with an extensive range of applications that can be found in several domains, such as biomedical diagnostics in healthcare and human–machine interactions. Recent works in SER have been focused on end-to-end deep neural networks (DNNs). However, the scarcity of emotion-labeled speech datasets inhibits the full potential of training a deep network from scratch. In this paper, we propose new approaches for classifying emotions from speech by combining conventional mel-frequency cepstral coefficients (MFCCs) with image features extracted from spectrograms by a pretrained convolutional neural network (CNN). Unlike prior studies that employ end-to-end DNNs, our methods eliminate the resource-intensive network training process. By using the best prediction model obtained, we also build an SER application that predicts emotio...

Wikipulse - Automatically Generating an Online Newspaper from Wikipedia Articles

More and more, user-generated content is complementing conventional journalism. While we don’t th... more More and more, user-generated content is complementing conventional journalism. While we don’t think that CNN or New York Times and its professional journalists will disappear anytime soon, formidable competition is emerging through humble Wikipedia editors. In earlier work (Becker 2012), we found that entertainment and sports news appeared on average about two hours earlier on Wikipedia than on CNN and Reuters online. In this project we build a news-reader that automatically identifies late-breaking news among the most recent Wikipedia articles and then displays it on a dedicated Web site.

Measuring Moral Values with Smartwatch-Based Body Sensors

In this research project we predict the moral values of individuals through their body movements ... more In this research project we predict the moral values of individuals through their body movements measured with the sensors of a smartwatch. The personal moral values are assessed using the Schwartz value theory, which proposes two dimensions of universal values (open to change versus conservative, self-enhancement versus self-transcendence). Data for all variables are gathered through the Happimeter, a smartwatch-based body-sensing system. Through multilevel mixed-effects generalized linear models, our results show that sensor and mood factors predict a person’s values. We utilized three methods to investigate the relationship between the Big Five personality traits (OCEAN: openness, conscientiousness, extraversion, agreeableness, and neuroticism) of a person and their Schwartz values. This research highlights the use of recent technological advances for studying a person’s values from an integrated perspective, combining body sensors and mood states to investigate individual behavi...

Dynamically Adapting the Environment for Elderly People Through Smartwatch-Based Mood Detection

Studies on Entrepreneurship, Structural Change and Industrial Dynamics, 2018

The ageing population and age-related diseases are some of the most urgent challenges in healthca... more The ageing population and age-related diseases are some of the most urgent challenges in healthcare. This leads to an increasing demand in innovative solutions to afford a healthy and safe lifestyle to the elderly. Towards this goal, the City4Age project, funded by the Horizon 2020 Programme of the European Commission, focuses on IoT-based personal data capture, supporting smart cities to empower social/health services. This paper describes the combination of the smartwatch-based Happimeter with City4Age data capture technology. Through measuring the mood of the wearer of the smartwatch, a signal is transmitted to the Philips Hue platform, enabling mood controlled lighting. Philips Hue allows the wireless remote control of energy-efficient LED light bulbs. Thus, measuring the mood through the Happimeter, the living environment for elderly people can be dynamically adapted. In our experiments, by changing colors and brightness of light bulbs using the Philips Hue platform, their quality of life can be improved. A validation test will be done in the context of the City4Age project, involving 31 elderly people living in a Southern Italian city.

Decoding Smartwatch Body Signals for Personal Trait Prediction

Body signals appear to be surprisingly informative in reflecting long-term personal traits (Gloor... more Body signals appear to be surprisingly informative in reflecting long-term personal traits (Gloor et al. 2010). In this project we use the Happimeter, a smartwatch based system to correlate body-signals with mood states (Budner et al. 2017) Based on the data collected by Pebble smartwatches from over 200 individuals during a year, we are able to develop statistical learning models that have substantial predictive power over the users’ personal traits. In this short paper we describe two studies, (1) predicting FFI personality characteristics (McCrae & Costa 2003), and (2) individual creativity measured with the Torrance test (Torrance 1980).

New Media in (Computer) Science Teaching at University Level (Dagstuhl Seminar 98051)

Wearable Technology for Assessment and Surgical Assistance in Minimally Invasive Surgery

Advances in Minimally Invasive Surgery [Working Title], 2021

Wearable technology is an emerging field that has the potential to revolutionize healthcare. Adva... more Wearable technology is an emerging field that has the potential to revolutionize healthcare. Advances in sensors, augmented reality devices, the internet of things, and artificial intelligence offer clinically relevant and promising functionalities in the field of surgery. Apart from its well-known benefits for the patient, minimally invasive surgery (MIS) is a technically demanding surgical discipline for the surgeon. In this regard, wearable technology has been used in various fields of application in MIS such as the assessment of the surgeon’s ergonomic conditions, interaction with the patient or the quality of surgical performance, as well as in providing tools for surgical planning and assistance during surgery. The aim of this chapter is to provide an overview based on the scientific literature and our experience regarding the use of wearable technology in MIS, both in experimental and clinical settings.

Measuring Human-Animal Interaction with Smartwatches: An Initial Experiment

Studies on Entrepreneurship, Structural Change and Industrial Dynamics, 2019

The paper describes and evaluates an explorative approach to quantify the relationship between tr... more The paper describes and evaluates an explorative approach to quantify the relationship between trainable animals and their owners. Data on human-animal-interaction has been collected by using Pebble smartwatches and by observing different kinds of animal training sessions. Tracking movement of horses and dogs with the Pebble Watch, the watch was able to measure data on horses, but not on dogs. Besides the breed and behavior of the animal, weather conditions and the way of attaching the Pebble influenced the measurement quality. In summary, the experiment indicates that there might be a connection between the heart rate (BPM), the average movement (VMC) and the mood data (pleasance and activation) of an animal and its owner during training sessions.

The Emergence of Rotating Leadership for Idea Improvement in a Grade 1 Knowledge Building Community

Designing Networks for Innovation and Improvisation, 2016

The purpose of this study is to elaborate theory-driven models of collaborative engagement in Kno... more The purpose of this study is to elaborate theory-driven models of collaborative engagement in Knowledge Building/knowledge creation. Assessment methods from Collaborative Innovation Network theory and Knowledge Building theory were integrated to investigate the phenomenon of collective responsibility for knowledge advancement as grade 1 students engaged in creative work with ideas in Knowledge Forum. At the group level, temporal network analyses were conducted, followed by discourse analysis of student notes in Knowledge Forum. At the individual level, social network analyses were conducted, followed by content analysis of student portfolios. Results indicate that overall, the student network was relatively decentralized, with many students rotating leadership at different points in time. Student notes and portfolios suggest that community knowledge (i.e., shared ideas, theories, and explanations) became increasingly complex and sophisticated over time. Educational implications are discussed within the context of redesigning schools to increase the knowledge-creating capacity of students.