everyone our work is real-time on gesture recognition in smart home scenarios with
minimum interfaith sensing we are from beijing university of post and telecommunications china
in air just cognition is a key human computer interaction that brings
neutral user experiences like user can control their home appliance remotely by only swiping her
hand traditional work utilize sooners wearables or cameras as stress straight sensors but they
have multiple shortcomings limited sensing range the discomfort of wearing contact
and the risk of privacy leakage respectively recently recent researchers also used wi-fi
signals to recognize gestures but the solutions usually cannot recognize fine-grained gestures and
are you are also vulnerable to strong interference from furniture chain and other moving people etc
beyond the wi-fi signals millimeter wave represents a new sensing medium with the potential
of fine-grained and robust sensing ability besides as a main venue of the emerging five generation
technology mma radios are anticipated to be encrypted on massive iot devices or smartphones
for instance um mm-hmm router has already been integrated into the recent released smartphone
google picker 4 with the promise of being a ubiquitous sensing tool
in this pair we proposed a new meter wave based gesture recognition system called
m-home jazz aiming at timely recognizing fine-grained arm gestures presented in
practical smart home scenarios along with complex interference to achieve this goal and home jazz
overcomes three main challenges firstly most of them focus on the neural range scenarios
within only 50 centimeters although the toolbox has benefited the detection range that only can
recognize whole body of human activities like jumps cord or human giant compared with the
large whole body activities the arm gestures only consist of the smaller motions of multiple
arm joints in different positions to overcome the challenge we construct a noble concentrate
position doppler profile to represent the unique features from different arm joints
besides to analyze the extracted profile we propose a shallow neural network classifier
to achieve the cura accurate recognition which only consists of three convolution network layers
secondly surrounding concurrent movements and multipath effect severely undermine gesture
recognition performance to handle the problem we first separate the reflection points based
on their spatial relationship and thus acquire a few potential user clusters then we eliminate
the interference from each user's nearby reflector than to a key inside the reflector may generate a
ghost shadow performing the mirror-like symmetric gesture synchronously with a real user but a
coexisting new nearby real user barely keeps such symmetrical parts in practice besides owing to the
special information of the reflection points i'm just just can associate with a concurrent
just with the position position of different users which benefits more interesting personalized human
computer interaction applications thirdly most of existing solutions are operated in an offline way
assuming the gesture symbols are segmented and prepared in advance however in practice
you should perform gesture continuous and that is a non-trivial challenge to distill this
and the segment are accurate and timely in this work we use a slider window to collect the gesture
features for a trained hidden markov model so as to ensure accurate gesture segmentation based on
this method amphomges can be aware of a user's status rather than performing a specific gesture
or making other movements thus can avoid an accurate response then to finally judge
a runtime system response we design a voting mechanism to ensure this except uh the above
three solutions we also provide an abundant mm with gesture data set we predefined the 10 model
joint arm gesture to train mm-js by recruiting uh 25 volunteers including 11 males and 14 females
um they asked to perform each prefine gesture 30 times and the training data settings are shipped
across 97 days and accumulates over 12 22 thousand samples in total the data details are available on
the our website and then we measure the user perception of home jazz by experiencing the system
in person as a baseline we also implement a voice control smart speaker to control its built-in
background music functions and also a directly connected lightnings for this uh we recruit 21
families to try i'm just i'm just out and the quantitative experience using a liquor scale
questionnaire from the results amphem just has a more convenient experience better flexibility and
better robustness on the basis of similar accuracy therefore mhm just had the potential to enable
a convenient smart home gesture based human computer interaction okay thank you very much