if you want the project pls call @8125424511
HUMAN ACTIVITY RECOGNITION
ABSTRACT
Human activity recognition, or HAR for short, is a broad
field of study concerned with identifying the specific movement or action of a
person based on sensor data.
The sensor data may be remotely recorded, such as video,
radar, or other wireless methods. It contains data generated from accelerometer,
gyroscope and other sensors of Smart phone to train supervised predictive
models using machine learning techniques like SVM , Random forest and decision tree
to generate a model. Which can be used to predict the kind of movement being
carried out by the person, which is divided into six categories walking,
walking upstairs, walking down-stairs, sitting, standing and laying?
MLM and SVM achieved accuracy of more than 99.2% in the
original data set and 98.1% using new feature selection method. Results show
that the proposed feature selection approach is a promising alternative to
activity recognition on smart phones.
Physical activity is well-known by the
general public to be crucial for leading a healthy life. Thus, researchers are
seeking a better understanding of the relationship between physical activity
and health. Precise recording of the conducted activities is an essential
requirement of their research. (Bauman et al., 2006) This data can be used to
design and construct activity recognition systems. These systems allow
physicians to check the recovery development of their patients automatically
and constantly (da Costa Cachucho et al., 2011). Another rising concern is the
sedentary life many people live, due to the shift in lifestyle occurring in the
modern world, where work and leisure tend to be less physically demanding
(Gyllensten, 2010). Several reports have already found links between common
diseases and physical inactivity (Preece et al., 2009). Thus, activity
recognition can be used by recommender systems to help the users track their
daily physical activity and promote them to increase their activity level. With
the recent progress in wearable technology, unobtrusive and mobile activity
recognition has become reasonable. With this technology, devices like
smartphones and smartwatches are widely available, hosting a wide range of
built-in sensors, at the same time, providing a large amount of computation
power. Overall, the technological tools exist to develop a mobile, unobtrusive
and accurate physical activity recognition system. Therefore, the realization
of recognizing the individuals’ physical activities while performing their
daily routine has become feasible. So far, no-one has investigated the usage of
light-weight devices for recognizing human activities. An activity recognition
system poses several main requirements. First, it should recognize activities
in real-time. This demands that the features used for classification are
computable in real-time. Moreover, short window durations must be employed to
avoid delayed response. Finally, the classification schemes should be simple,
light-weight and computationally inexpensive to be able to run on hand-held
devices.
MODULES
The activity recognition process is described,
containing four main stages.
1.
Data
Collection: The first step is to collect
multivariate time series data from the phone’s and the watch’s sensors. The
sensors are sampled with a constant frequency of 30 Hz. After that, the sliding
window approach is utilized for segmentation, where the time series is divided
into subsequent windows of fixed duration without interwindow gaps (Banos et
al., 2014). The sliding window approach does not require preprocessing of the
time series, and is therefore ideally suited to real-time applications.
2.
Preprocessing:
Filtering is performed afterwards to
remove noisy values and outliers from the accelerometer time series data, so
that it will be appropriate for the feature extraction stage. There are two
basic types of filters that are usually used in this step: average filter
(Sharma et al., 2008) or median filter (Thiemjarus, 2010). Since the type of
noise dealt with here is similar to the salt and pepper noise found in images,
that is, extreme acceleration values that occur in single snapshots scattered
throughout the time series. Therefore, a median filter of order 3 (window size)
is applied to remove this kind of noise.
3.
Feature Extraction:
Here, each resulting segment will be summarized by a fixed number of features,
i.e., one feature vector per segment. The used features are extracted from both
time and frequency domains. Since, many activities have a repetitive nature,
i.e., they consist of a set of movements that are done periodically like
walking and running. This frequency of repetition, also known as dominant
frequency, is a descriptive feature and thus, it has been taken into
consideration.
4.
Standardization:
Since, the time domain features are measured in (m/s 2 ), while the frequency
ones in (Hz), therefore, all features should have the same scale for a fair
comparison between them, as some classification algorithms use distance
metrics. In this step, Z-Score standardization is used, which will transform
the attributes to have zero mean and unit variance, and is defined as
xnew
= (x−µ)/ σ
where µ and σ are the attribute’s mean and standard
deviation respectively (Gyllensten, 2010).
EXISTING
SYSTEM
Several investigations have considered the use of widely
available mobile devices. Ravi et. al. collected data from only two users
wearing a single accelerometer-based device and then transmitted this data to
the phone carried by the user (Ravi et al.,2005). Lester et. al. used
accelerometer data from a small set of users along with audio and barometric
sensor data to recognize eight daily activities (Lesteret al., 2006). However,
the data was generated using distinct accelerometer-based devices worn by the
user and then sent to the phone for storage.
Some studies took advantage of the sensors incorporated into
the phones themselves. Yang developed an activity recognition system using a
smart-phone to distinguish between various activities (Yang, 2009). However,
stair climbing was not considered and their system was trained and tested using
data from only four users. Brezmes et. al. developed a real-time system for
recognizing six user activities (Brezmeset al., 2009). In their system, an
activity recognition model is trained for each user, i.e., there is no
universal model that can be applied to new users for whom no training data
exists. Bayat et al. gathered acceleration data from only four participants,
performing six activities. (Bayat et al., 2014) Shoaib et al. evaluated
different classifiers by collecting data of smart-phone accelerometer,
gyroscope, and magnetometer for four subjects, performing six activities.
(Shoaib et al., 2013).
PROPOSED
SYSTEM
The purpose of being able to classify what activity a person
is undergoing at a given time is to allow computers to provide assistance and
guidance to a person prior to or while undertaking a task.
The difficulty lies in how diverse our movements are as we
perform our day-to-day tasks.
There have been many attempts to use the various machine
learning algorithms to accurately classify a person’s activity, so much so that
Google have created an Activity Recognition API for developers to embed into
their creation of mobile applications.
CONCLUSION
In this paper, a platform to combine
sensors of smartphones and smartwatches to classify various human activities
was proposed. It recognizes activities in real-time Moreover, this approach is
light-weight, computationally inexpensive, and able to run on handheld devices.
The results showed that there is no clear winner, but naive Bayes performs best
in our experiment in both the classification accuracy and efficiency. The
overall accuracy lies between 84.6% and 89.4%, at which the differences are
negligible. Thus, this platform is able to recognize various human activities.
However, all of the tested classifiers confused walking and using the stairs
activities. The second conclusion is that adding the smartwatch’s sensor data
to the recognition system improves it’s accuracy with at least six percentage
point. Finally, it is computations that the best sampling frequency is in the
field of 10 Hz. Some questions still require to be answered. Most important is
the conducting of larger experiments with more people in order to perform more
robust evaluation to clearify if indeed one method is better than the other, or
whether, any off-the-shelf method can do well in this classification task. This
work could be furhter extended by incorporating more sensors (e.g. heart rate
sensor), recognizing high-level activities (e.g. shopping or eating dinner) or
extrapolating these trained classifiers to other people.
BIBLIOGRAPHY
[1] Banos, O., Galvez, J.-M., Damas, M.,
Pomares, H., and Rojas, I. (2014). Window size impact in human activity
recognition. Sensors, 14(4):6474–6499.
[2] Bao, L. and Intille, S. S. (2004).
Activity recognition from user-annotated acceleration data. In Pervasive
computing, pages 1–17. Springer.
thank you for your comment
pls call me on 8125424511