ABSTRACT
Recently a new class of personal assistants that are capable of addressing users' information needs proactively is emerging.
Users' information needs may include timely notifications about a certain context such as location, social interactions with other people, weather, other events, etc. Personal assistants can assist people by recommending the right information at just the right time and help them in accomplishing tasks. Because of the ubiquitous nature of mobile personal assistants, they have a broad range of potential capabilities. One of these potential capabilities is to carry out sophisticated tasks for supporting failing memories. Such support of human memory has been thus far limited, merely to setting reminders and calendar events.
In this paper, we present our work on developing a cutting-edge personal assistant for supporting failing memories in every day social interactions. Specifically, we envision a personal assistant that can anticipate the parts of a past conversation that you are likely to forget, hence remind you about them. Our experimental results on a real-world dataset of meetings reveals evidence that developing such systems is viable and can produce promising results.
- Ioannis Arapakis, Joemon M. Jose, and Philip D. Gray. Affective Feedback: An Investigation into the Role of Emotions in the Information Seeking Process Proceedings of the 31st Annual International ACM SIGIR Conference (SIGIR '08). 395--402. Google ScholarDigital Library
- Seyed Ali Bahrainian and Fabio Crestani 2016. Cued Retrieval of Personal Memories of Social Interactions Proceedings of the First Workshop on Lifelogging Tools and Applications (LTA '16). 3--12. Google ScholarDigital Library
- Seyed Ali Bahrainian and Fabio Crestani 2017. Are conversation logs useful sources for generating memory cues for recalling past memories? Proceedings of the Second Workshop on Lifelogging Tools and Applications (LTA '17).Google ScholarDigital Library
- Seyed Ali Bahrainian and Andreas Dengel 2015. Sentiment analysis of texts by capturing underlying sentiment patterns Web Intelligence, Vol. Vol. 13. 53--68.Google Scholar
- Jason J Braithwaite, Derrick G Watson, Robert Jones, and Mickey Rowe. A guide for analysing electrodermal activity (EDA) & skin conductance responses (SCRs) for psychological experiments. Handbook of Psychophysiology (????), 1017--1034.Google Scholar
- Yi Chen and Gareth JF Jones 2010. Augmenting human memory using personal lifelogs. Proceedings of the 1st augmented human international conference. Google ScholarDigital Library
- Anne M.; Filion Diane L. Cacioppo John T. (Ed); Tassinary Louis G. (Ed); Berntson Gary G. Dawson, Michael E.; Schell. 2000. Sentiment in Short Strength Detection Informal Text. Handbook of psychophysiology, 2nd ed. (2000), 200--223.Google Scholar
- Ashlee Edwards, Diane Kelly, and Leif Azzopardi. The Impact of Query Interface Design on Stress, Workload and Performance. 691--702.Google Scholar
- Cathal Gurrin, Alan F. Smeaton, and Aiden R. Doherty. 2014. LifeLogging: Personal Big Data. Foundations and Trends in Information Retrieval (2014). Google ScholarDigital Library
- Jennifer Healey and Rosalind Picard 1998. Digital processing of affective signals. In Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing, Vol. Vol. 6. 3749--3752.Google ScholarCross Ref
- Marti A. Hearst. 1997. TextTiling: Segmenting Text into Multi-paragraph Subtopic Passages. Comput. Linguist., Vol. 23, 1 (1997), 33--64. Google ScholarDigital Library
- Thomas Hofmann. 1999. Probabilistic Latent Semantic Indexing. In Proc. of International ACM SIGIR Conference (SIGIR '99). 50--57. Google ScholarDigital Library
- A. Jaimes, H. Bourlard, S. Renals, and J. Carletta. 2007. Recording, Indexing, Summarizing, and Accessing Meeting Videos: An Overview of the AMI Project 14th International Conference of Image Analysis and Processing - Workshops (ICIAPW 2007). 59--64. Google ScholarDigital Library
- Ro Jaimes, Kengo Omura, Takeshi Nagamine, and Kazutaka Hirata. 2004. Memory cues for meeting video retrieval. In In Proceedings CARPE 04. 74--85. Google ScholarDigital Library
- Vaiva Kalnikaité and Steve Whittaker. Software or Wetware?: Discovering when and Why People Use Digital Prosthetic Memory Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (CHI '07). 71--80. Google ScholarDigital Library
- Basel Kikhia, Josef Hallberg, Kåre Synnes, and Zaheer Ul Hussain Sani 2009. Context-aware life-logging for persons with mild dementia Engineering in Medicine and Biology Society, 2009.Google Scholar
- Mik Lamming and Mike Flynn 1994. Forget-me-not: Intimate computing in support of human memory. 125--128.Google Scholar
- Matthew L. Lee and Anind K. Dey 2008. Wearable experience capture for episodic memory support 12th IEEE International Symposium on Wearable Computers (ISWC 2008). Seoul, Korea, 107--108. Google ScholarDigital Library
- Yashar Moshfeghi and Joemon M. Jose 2013. An Effective Implicit Relevance Feedback Technique Using Affective, Physiological and Behavioural Features. In Proceedings of the 36th International ACM SIGIR Conference (SIGIR '13). 133--142. Google ScholarDigital Library
- Marcin Pietrzykowskiand and Wojciech Salabun 2014. Applications of Hidden Markov Model: state-of-the-art International Journal of Computer Technology and Applications.Google Scholar
- Lawrence R. Rabiner. 1990. A Tutorial on Hidden Markov Models and Selected Applications in Speech Recognition. Readings in Speech Recognition. Google ScholarDigital Library
- Bradley Rhodes and Thad Starner. Remembrance Agent: A continuously running automated information retrieval system.Google Scholar
- B. J. Rhodes and P. Maes 2000. Just-in-time Information Retrieval Agents. IBM Syst. J. Vol. 39 (2000), 685--704. Google ScholarDigital Library
- Abigail Sellen, Andrew Fogg, Mike Aitken, Steve Hodges, Carsten Rother, and Kenneth R. Wood. 2007. Do life-logging technologies support memory for the past?: an experimental study using sensecam In Proc. of the Conference on Human Factors in Computing Systems. 81--90. Google ScholarDigital Library
- Yu Sun, Nicholas Jing Yuan, Yingzi Wang, Xing Xie, Kieran McDonald, and Rui Zhang. 2016. Contextual Intent Tracking for Personal Assistants Proceedings of the 22Nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD '16). 273--282. Google ScholarDigital Library
- Mike Thelwall, Kevan Buckley, Georgios Paltoglou, Di Cai, and Arvid Kappas 2010. Sentiment in Short Strength Detection Informal Text. J. Am. Soc. Inf. Sci. Technol. Vol. 61, 12 (Dec. 2010), 2544--2558. Google ScholarDigital Library
- Tobias Grossmann Vaish, Amrisha and Amanda Woodward 2008. Not All Emotions Are Created Equal: The Negativity Bias in Social-Emotional Development. Psychological bulletin 134.3 (2008), 383--403.Google Scholar
- A. Waibel, T. Schultz, M. Bett, M. Denecke, R. Malkin, I. Rogina, R. Stiefelhagen, and Jie Yang 2003. SMaRT: the Smart Meeting Room Task at ISL. In Acoustics, Speech, and Signal Processing, 2003. Proceedings. (ICASSP '03). 2003 IEEE International Conference on, Vol. Vol. 4. IV--752--5 vol.4.Google Scholar
- Ryuichi et. al. Yoshida. Feasibility study on estimating visual attention using electrodermal activity 8th International Conference on Sensing Technology. 2014.Google Scholar
Index Terms
- Towards the Next Generation of Personal Assistants: Systems that Know When You Forget
Recommendations
Analyzing Deaf and Hard-of-Hearing Users’ Behavior, Usage, and Interaction with a Personal Assistant Device that Understands Sign-Language Input
CHI '22: Proceedings of the 2022 CHI Conference on Human Factors in Computing SystemsAs voice-based personal assistant technologies proliferate, e.g., smart speakers in homes, and more generally as voice-control of technology becomes increasingly ubiquitous, new accessibility barriers are emerging for many Deaf and Hard of Hearing (DHH) ...
Deaf Users’ Preferences Among Wake-Up Approaches during Sign-Language Interaction with Personal Assistant Devices
CHI EA '21: Extended Abstracts of the 2021 CHI Conference on Human Factors in Computing SystemsPersonal-assistant devices like Amazon Alexa and Google Assistant are increasingly popular among consumers. Users activate these systems through some type of wake-up approach, e.g. using a wake-word “Alexa” or “Ok, Google.” Voice-based interaction poses ...
Understanding deaf and hard-of-hearing users' interest in sign-language interaction with personal-assistant devices
W4A '21: Proceedings of the 18th International Web for All ConferenceProliferation of voice-controlled personal-assistant devices poses accessibility barriers for Deaf and Hard of Hearing (DHH) users. In this mixed interview (N=21) and survey (N=86) study, DHH American Sign Language (ASL) signers reported little ...
Comments