Activity gesture spotting using a threshold model based on Adaptive Boosting

Krishnan, Narayanan C.; Lade, Prasanth; Panchanathan, Sethuraman

doi:10.1109/icme.2010.5583013

Cited by 16 publications

(10 citation statements)

References 9 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…When 3-axis accelerometers are distributed over an individual’s body then each sensor can provide information about the orientation and movement of the corresponding body part. Researchers commonly use these inertial measurement units to recognize ambulatory movements (e.g., walking, running, sitting, climbing, and falling) [40], [41], posture [42] and gestures [43], [44], [45], [46]. …”

Section: Algorithms and Methodsmentioning

confidence: 99%

A Survey on Ambient Intelligence in Healthcare

et al. 2013

View full text Add to dashboard Cite

Ambient Intelligence (AmI) is a new paradigm in information technology aimed at empowering people’s capabilities by the means of digital environments that are sensitive, adaptive, and responsive to human needs, habits, gestures, and emotions. This futuristic vision of daily environment will enable innovative human-machine interactions characterized by pervasive, unobtrusive and anticipatory communications. Such innovative interaction paradigms make ambient intelligence technology a suitable candidate for developing various real life solutions, including in the health care domain. This survey will discuss the emergence of ambient intelligence (AmI) techniques in the health care domain, in order to provide the research community with the necessary background. We will examine the infrastructure and technology required for achieving the vision of ambient intelligence, such as smart environments and wearable medical devices. We will summarize of the state of the art artificial intelligence methodologies used for developing AmI system in the health care domain, including various learning techniques (for learning from user interaction), reasoning techniques (for reasoning about users’ goals and intensions) and planning techniques (for planning activities and interactions). We will also discuss how AmI technology might support people affected by various physical or mental disabilities or chronic disease. Finally, we will point to some of the successful case studies in the area and we will look at the current and future challenges to draw upon the possible future research paths.

show abstract

Section: Algorithms and Methodsmentioning

confidence: 99%

A Survey on Ambient Intelligence in Healthcare

et al. 2013

View full text Add to dashboard Cite

show abstract

“…To overcome the instability of HMM probabilities, [17] uses similarity to known examples as a preprocessing filter, [19] uses boosting to improve threshold models, and [1] proposes online threshold adaptation. Outside of HMMs, other proposed spotting approaches have been based on dynamic time warping [26] and string matching [32].…”

Section: Gesture Spottingmentioning

confidence: 99%

RecoFit

Morris

Saponas

Guillory

et al. 2014

Proceedings of the SIGCHI Conference on Human Factors in Computing Systems

160

View full text Add to dashboard Cite

Although numerous devices exist to track and share exercise routines based on running and walking, these devices offer limited functionality for strength-training exercises. We introduce RecoFit, a system for automatically tracking repetitive exercises -such as weight training and calisthenics -via an arm-worn inertial sensor. Our goal is to provide real-time and post-workout feedback, with no user-specific training and no intervention during a workout. Toward this end, we address three challenges: (1) segmenting exercise from intermittent non-exercise periods, (2) recognizing which exercise is being performed, and (3) counting repetitions. We present cross-validation results on our training data and results from a study assessing the final system, totaling 114 participants over 146 sessions. We achieve precision and recall greater than 95% in identifying exercise periods, recognition of 99%, 98%, and 96% on circuits of 4, 7, and 13 exercises respectively, and counting that is accurate to ±1 repetition 93% of the time. These results suggest that our approach enables a new category of fitness tracking devices.

show abstract

“…The first is the problem of activity recognition itself (Blanke and Schiele, 2010; Calatroni, Roggen and Tröster, 2011; Chieu, Lee and Kaelbling, 2006; Hachiya, Sugiyama and Ueda, 2012; Krishnan, Lade and Panchanathan, 2010; Kurz, Hölzl, Ferscha, Calatroni, Roggen and Tröster, 2011; Roggen, Frster, Calatroni and Trster, 2011; Venkatesan, Krishnan and Panchanathan, 2010; Zhao, Chen, Liu and Liu, 2010; Zhao, Chen, Liu, Shen and Liu, 2011), and the second is the problem of user localization, which can then be used to increase the accuracy of the activity recognition algorithm (Pan, Tsang, Kwok and Yang, 2011; Pan, Kwok, Yang and Pan, 2007; Pan, Shen, Yang and Kwok, 2008; Pan, Zheng, Yang and Hu, 2008; Zheng, Pan, Yang and Pan, 2008). Both problems present interesting challenges for transfer learning.…”

Section: Modalitymentioning

confidence: 99%

“…Several papers focus specifically on transferring across time differences (Krishnan et al, 2010; Pan et al, 2011; Pan et al, 2007; Venkatesan et al, 2010), differences between people (Chattopadhyay, Krishnan and Panchanathan, 2011; Hachiya et al, 2012; Rashidi and Cook, 2009; Zhao et al, 2011), and differences between devices (Zhao et al, 2010; Zheng et al, 2008). …”

Section: Physical Setting Differencesmentioning

confidence: 99%

Transfer learning for activity recognition: a survey

2013

View full text Add to dashboard Cite

Many intelligent systems that focus on the needs of a human require information about the activities being performed by the human. At the core of this capability is activity recognition, which is a challenging and well-researched problem. Activity recognition algorithms require substantial amounts of labeled training data yet need to perform well under very diverse circumstances. As a result, researchers have been designing methods to identify and utilize subtle connections between activity recognition datasets, or to perform transfer-based activity recognition. In this paper we survey the literature to highlight recent advances in transfer learning for activity recognition. We characterize existing approaches to transfer-based activity recognition by sensor modality, by differences between source and target environments, by data availability, and by type of information that is transferred. Finally, we present some grand challenges for the community to consider as this field is further developed.

show abstract

Activity gesture spotting using a threshold model based on Adaptive Boosting

Cited by 16 publications

References 9 publications

A Survey on Ambient Intelligence in Healthcare

A Survey on Ambient Intelligence in Healthcare

RecoFit

Transfer learning for activity recognition: a survey

Contact Info

Product

Resources

About