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PROSELF: Semi-automated Self-Tracking Systems to Improve Personal Productivity

Applicant Santini Silvia
Number 197242
Funding scheme Project funding (Div. I-III)
Research institution Faculty of Informatics Universitá della Svizzera Italiana (USI)
Institution of higher education Università della Svizzera italiana - USI
Main discipline Information Technology
Start/End 01.10.2021 - 30.09.2025
Approved amount 898'950.00
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Keywords (7)

Activity recognition and modelling; Mobile anticipatory systems; Workplace productivity; Pervasive data science; Ubiquitous computing; Mobile sensing and computing; Context aware systems

Lay Summary (Italian)

Gli strumenti informatici, come ad esempio computer, ma anche smartphones, smartwatches, e altri dispositivi, permettono oggigiorno a molti lavoratori di svolgere la loro professioni quasi ovunque e in qualunque momento. Gestire in modo efficace e sostenibile questa onnipresente possibilità di lavorare è necessario, sia per garantire la buona salute fisica e mentale del lavoratore, sia per preservarne la produttività.
Lay summary

Soggetto e obiettivo

Ci rivolgiamo ai cosiddetti ``knowledge workers’’, ovvero quei lavoratori la cui professione consiste nell’uso e sviluppo della conoscenza, e che godono di un alto grado di autonomia nell’organizzare il proprio lavoro. Gli strumenti informatici che tali lavoratori utilizzano, possono essere utilizzati come sensori, che captano lo stato del lavoratore e lo supportano per migliorare la sua produttività e benessere psicofisico. A questo scopo è necessario: (1) capire quali dati possono essere rilevati utilizzando gli strumenti; (2) sviluppare modelli matematici che permettano di stimare lo stato del lavoratore – ad esempio il suo livello di attenzione o l’attività che sta svolgendo – utilizzando i dati raccolti; (3) sviluppare metodi e sistemi che possano intervenire attivamente e aiutare il lavoratore a migliorare la sua quotidianità.

Contesto socio-scientifico

Il nostro lavoro permetterà di sviluppare una nuova metodologia per realizzare sistemi personalizzati di supporto ai lavoratori della conoscenza, con lo scopo di migliorare sia il loro benessere che la loro produttività.

Parole chiave

Riconoscimento automatico delle attività umane, sistemi sensibili al contesto, modelli di predizione delle attività umane, produttività e benessere sul posto di lavoro, sistemi mobili e indossabili, scienza dei dati.

Direct link to Lay Summary Last update: 26.09.2021

Responsible applicant and co-applicants


Project partner


PROSELF aims at investigating how emerging mobile and wearable technology can help provide an understanding of what makes people feel (and be) productive, and subsequently, to assist users with managing their productivity on a daily basis.The use of technology to monitor workplace activities is not new but often raises concerns regarding worker rights and privacy - invoking alarmist headlines and a dystopian vision of “big brother” surveillance. This is because such technology has typically been driven by employers to exercise control or evidence efficiency savings in the context of low-skill workplaces. In PROSELF, we instead advocate a focus on knowledge workers with the goal of empowering employees to self-reflect upon their own activities, well-being, and performance at work.Technologies that allow people to self-monitor their work activity already exist, e.g., to track and visualize their computer applications usage or to trigger an alarm if one spends too much time on social networks. A wealth of so-called “productivity apps”, e.g., for to-do-list management or collaborative work, allow individuals to organize their work efficiently. Yet while all these tools help people to better monitor and control work activities, they typically neglect the wider context in which these activities take place. In particular, existing systems miss the opportunity to consider environmental, behavioral, and psychological factors in one’s feeling of (and indeed, observed) performance at work, and thus fail to help users understand why one day felt more productive than another.In order to be truly effective and drive productivity improvement, self-monitoring technologies for the workplace must not only collect and visualize data, but also reason over it and use this cognizance to drive anticipatory actions to both improve the self-monitoring and promote productivity improvements. To this end, PROSELF will: (1) Provide novel methods to enable self-monitoring at work in a semi-automated manner; (2) Develop a sound modeling framework to identify proxies of specific performance indicators in users’ context data; (3) Devise an adequate architecture to support the definition and execution of anticipatory decisions; and (4) Critically evaluate these approaches in-situ in a range of field studies.If successful, PROSELF will advance the understanding of how computing technologies can help individuals observe, balance, and improve their performance and well-being at the workplace. It will contribute a clear definition of the design space of self-monitoring systems for knowledge workers, as well as a sound methodology for their design and development. The validation of the proposed methods through large-scale field studies will give the obtained results the strength necessary for other researchers to build upon them. Ultimately, our work seeks to lay the foundations for new systems to help improve the effectiveness of knowledge workers - with significant consequential benefits for society at large.