Estimating Physical Characteristics with Body-worn Accelerometers Based on Activity Similarities

This paper describes our experimental investigation of the end user physical characteristics (e.g., gender, height, weight, dominant hand, and skill at sport) that can be successfully estimated solely from sensor data obtained during daily activities (e.g., walking and dish washing) from body-worn accelerometers. For this purpose we use the huge quantities of data that we have collected, which include 14,880 labeled activities obtained from 61 subjects. Our proposed method tries to estimate various kinds of characteristics based on our simple idea ‘When the activity sensor data of two users are similar, the physical characteristics of the two users may also be similar.’ We consider that estimating the end user’s physical characteristics will enable us to realize new kinds of applications that automatically recommend information/services to an end user according to her estimated physical characteristics such as gender and weight.

[1]  S. Blair,et al.  The utility of the Digi-walker step counter to assess daily physical activity patterns. , 2000, Medicine and science in sports and exercise.

[2]  Kirsi Helkala,et al.  Biometric Gait Authentication Using Accelerometer Sensor , 2006, J. Comput..

[3]  Tanzeem Choudhury,et al.  Passive and In-Situ assessment of mental and physical well-being using mobile sensors , 2011, UbiComp '11.

[4]  John Riedl,et al.  Item-based collaborative filtering recommendation algorithms , 2001, WWW '01.

[5]  อนิรุธ สืบสิงห์,et al.  Data Mining Practical Machine Learning Tools and Techniques , 2014 .

[6]  L. Mâsse,et al.  Physical activity in the United States measured by accelerometer. , 2008, Medicine and science in sports and exercise.

[7]  Jennifer Healey,et al.  A Long-Term Evaluation of Sensing Modalities for Activity Recognition , 2007, UbiComp.

[8]  Ling Bao,et al.  Activity Recognition from User-Annotated Acceleration Data , 2004, Pervasive.

[9]  Sangeeta Bhattacharya,et al.  Jog Falls: A Pervasive Healthcare Platform for Diabetes Management , 2010, Pervasive.

[10]  Heikki Ailisto,et al.  Identifying users of portable devices from gait pattern with accelerometers , 2005, Proceedings. (ICASSP '05). IEEE International Conference on Acoustics, Speech, and Signal Processing, 2005..

[11]  T. Baranowski,et al.  Adolescent patterns of physical activity differences by gender, day, and time of day. , 2005, American journal of preventive medicine.

[12]  Gerhard Tröster,et al.  What Do Sensors Know about Your Running Performance? , 2011, 2011 15th Annual International Symposium on Wearable Computers.

[13]  Mohammad Albaida.  Object-based Activity Recognition with Heterogeneous Sensors on Wrist , 2018 .

[14]  Paul Lukowicz,et al.  Where am I: Recognizing On-body Positions of Wearable Sensors , 2005, LoCA.

[15]  J. Petrofsky,et al.  Gait characteristics in people with type 2 diabetes mellitus , 2005, European Journal of Applied Physiology.

[16]  D. Rubin,et al.  Maximum likelihood from incomplete data via the EM - algorithm plus discussions on the paper , 1977 .

[17]  Michael L. Littman,et al.  Activity Recognition from Accelerometer Data , 2005, AAAI.

[18]  R. Klesges,et al.  A longitudinal analysis of the impact of dietary intake and physical activity on weight change in adults. , 1992, The American journal of clinical nutrition.

[19]  Kunsoo Shin,et al.  Estimation of Activity Energy Expenditure: Accelerometer Approach , 2005, 2005 IEEE Engineering in Medicine and Biology 27th Annual Conference.

[20]  Hao-Hua Chu,et al.  Phone-based gait analysis to detect alcohol usage , 2012, UbiComp.

[21]  Hassan Ghasemzadeh,et al.  Sport training using body sensor networks: a statistical approach to measure wrist rotation for golf swing , 2009, BODYNETS.

[22]  Igor Kononenko,et al.  Estimating Attributes: Analysis and Extensions of RELIEF , 1994, ECML.

[23]  Kaori Fujinami,et al.  Recognizing a Mobile Phone's Storing Position as a Context of a Device and a User , 2012, MobiQuitous.

[24]  Gary M. Weiss,et al.  Identifying user traits by mining smart phone accelerometer data , 2011, SensorKDD '11.