Patent Publication Number: US-2020275835-A1

Title: System and method for contact less control of an appliance technical field

Description:
TECHNICAL FIELD 
     The subject matter described herein, in general, relates to human-machine interaction technologies, and in particular, but not exclusively, to a non-contact gesture-based control technique for controlling an electronic device. 
     BACKGROUND 
     Technologies for contact-less interaction between humans and machines aim at providing control and operation of an electronic or electrical appliance without physical contact between a user and the appliance. Such technologies include gesture-based control, voice-based control, electroencephalogram (EEG) signal based control, or the like. For instance, the gesture-based technology works on a principle of sensing a gesture made by the user and operating the appliance in response to the gesture. On the other hand, EEG-based control may involve sensing brain or nerve signals, referred to as EEG signals, corresponding to the user&#39;s brain activity and controlling the appliance based on the sensed signals. 
    
    
     
       BRIEF DESCRIPTION OF DRAWINGS 
       The above and other features, aspects, and advantages of the subject matter will be better understood with regard to the following description, appended claims, and accompanying drawings where: 
         FIG. 1  illustrates an environment having remotely controlled appliances, in accordance with one implementation of the present subject matter. 
         FIG. 2 a    different designs of a wearable device, in accordance with one implementation of the present subject matter. 
         FIG. 2 b    illustrates the wearable device, in accordance with one implementation of the present subject matter. 
         FIG. 3  illustrates a method for contactless operation of the remotely controlled appliances, in accordance with one implementation of the present subject matter. 
         FIG. 4  illustrates a network environment, in accordance with one implementation of the present subject matter. 
     
    
    
     DETAILED DESCRIPTION 
     Conventional techniques use one type of the signal to operate the appliance. For example, gesture based control techniques utilizes motion signal to select and operate the appliance. In another example, EEG based control techniques utilizes EEG signals to select and operate the appliance. However, conventional techniques do not verify the selection based on the input signal. As a result, the selection made using conventional techniques are not accurate. Moreover, conventional techniques do not use information related to a position the user with respect to the appliance. There may be case where when two appliances are operated based on a single gesture. Since the conventional techniques did not determine if the user is in the vicinity of the first appliance or the second appliance, this may lead to a conflict so to which appliance the user wants to operate when the user makes the gesture to control one of the appliances. Thus, conventional contacts less controllers are less accurate in their operation. 
     The present subject matter relates to various aspects for providing contact less control of the appliances with precise selection. Techniques based on the present subject matter utilize two different types of signals to select an appliance. For example, motion of a user&#39;s body part is sensed and motion signals generated corresponding to the sensed motion, is used to select the appliance. In addition, the brain activity related to the same body part is also sensed and an EEG signal corresponding to the sensed movement is also used to select the appliance. Moreover, selections made using the motion based signal and the EEG signal are cross verified to finalise the selection of the appliance. Since the present subject matter utilizes different sources to select the appliance, the selection of the appliance is accurate. 
     One aspect of the present subject matter employs brainwave sensors that may sense the user&#39;s brain activity. In one example, the brainwave sensor may generate a first EEG signal based on a movement of a user&#39;s first body part, such as the user&#39;s arm, and the first EEG signal may facilitate in determining an orientation of the first body part so as to determine an appliance to which user intend to select. In addition, the brainwave sensor may generate a second EEG signal based on a movement of user&#39;s second body part, such as movement of fingers, and the second EEG signal may facilitate in determining a predefined control action for the appliance towards which the user is pointing. The present subject may also employ motion sensors that sense motion of the user&#39;s body part onto which the wearable device is worn. In one example, selection of the appliance based on the motion signal is performed in parallel to the selection made using the first EEG signal Since the selection made by the signal EEG signal is complemented by the selection made by the motion signal, the selection of the appliance made based on the present subject matter is accurate. 
     Another aspect of the subject matter relates to measuring a strength of a wireless signal transmitted by the appliance. Further, the strength of the wireless signal is indicative of a distance between the user and the appliance. Thus, a measure of the strength of the wireless signal is used to determine a distance between the user and the appliance. Thus, the techniques based on the present subject matter can determine the position of the user with respect to the appliance and accordingly may operate the appliance when the user is around the appliance. 
     The manner in which the wearable device operates shall be explained in details with respect to the accompanying figures. It should be noted that the description merely illustrates the principles of the present subject matter. It will thus be appreciated that those skilled in the art will be able to devise various arrangements that, although not explicitly described herein, embody the principles of the present subject matter and are included within its spirit and scope. Furthermore, all examples recited herein are principally intended expressly to be only for pedagogical purposes to aid the reader in understanding the principles of the present subject matter and the concepts contributed by the inventor(s) to furthering the art, and are to be construed as being without limitation to such specifically recited examples and conditions. Moreover, all statements herein reciting principles, aspects, and embodiments of the present subject matter, as well as specific examples thereof, are intended to encompass equivalents thereof. 
       FIG. 1  illustrates an environment  100 , in accordance with one implementation of the present subject matter. The environment  100  is a space where a user can exist and can operate the components present in the environment  100 . In one example, the environment  100  can be a room in a house, an office area, or the like. Further, it may be understood that different environments may exist. The environment  100  may include multiple remotely-controlled appliances  102 - 1 ,  102 - 2 ,  102 - 3 ; collectively referred to as  102  hereinafter. The remotely-controlled appliances  102  can be, but not limited to, a television, air conditioner, lighting devices, or the like. Generally, each of the remotely controlled appliances may include an appliance controller  104  for controlling an operation of the remotely controlled appliance  102 . In another implementation, the environment  100  may include a single appliance controller  104  that may be connected to all the remotely controlled appliances  102 . The appliance controller can be a handheld computing device, such as a smartphone, tablet, PDA, or the like. According to an aspect, the appliance controller  104  may include a microcontroller  104 - 1  that may perform control operation on the remotely controlled appliance  102 . In addition, the appliance controller  104  may also include a wireless module  104 - 2  that may receive instructions to control the remotely controlled appliance  102  via a wireless network connection. In one example, the wireless module  104 - 1  can be a Bluetooth transceiver or a Wi-Fi module. In the illustrated aspect, the appliance controller  104  may include other components, such as a power source to provide power the appliance controller  104 , switches for manual operation of the remotely controlled appliance  102 , and relay switches to control power surges in the remotely controlled appliance  102 . 
     According to an aspect, the environment  100  may also include a wearable device  108  that may be borne and used by a user to control various remotely controlled appliances  102  present in the environment  100 . In one example, the wearable device  108  may control the remotely controlled appliances  102  over the wireless network established between the wearable device  108  and the appliance controller  104 . For example, in case the first remotely controlled appliance  102 - 1  is a television, the wearable device  108  may turn ON/OFF, increase/decrease the volume, change the channel, or the like on the first remotely controlled appliance  102 - 1  based on the user&#39;s input. In the illustrated aspect, the wearable device  108  may select the remotely appliance  102  based on different types of inputs or signals. In one example, the wearable device  102  may use brainwave signals from the user&#39;s brain activity as well as gestures made by the user&#39;s body part to select the remotely controlled appliance  102 . Further, use different signals for selecting the remotely controlled appliance  102  at the same time makes the selection of the remotely controlled appliance  102  accurate. In addition, the wearable device  108  may also determine presence of the wearable device  100  in the environment  100 . Further, detection the presence of the wearable device  108  in the environment and the remotely controlled appliances  102  facilitates in improving the accuracy of the operation of the wearable  108 . The constructional details of the wearable device  108  and the manner by which the wearable device  108  operates will be explained later. 
       FIGS. 2 a  and 2 b    illustrates a wearable device  108 , in accordance with one implementation of the present subject matter. The wearable device  108  can be used to control the remotely controlled appliances  102  based on user&#39;s brain activity and body gestures. In an example, the wearable device  108  can be borne by the user for contactless interaction with a plurality of remotely controlled appliances present in the environment  100 . For instance, the wearable device  108  can be formed as a wrist-worn device  108 - 1 , a headgear  108 - 2 , or a necklace  108 - 3 . In the illustrated aspect, the wearable device  108  may include a motion sensor  202  that can sense a motion of the user&#39;s body part. For example, the motion sensor  202  in the wrist borne wearable device  108 - 1  may sense a gesture made by an arm of the user. Accordingly, the motion sensor  202  may generate a motion signal indicative of the sensed movement of the user&#39;s body part on which the wearable device  108  is worn. In case of the head borne wearable device  108 - 2 , the motion sensor  202  may sense a movement of head. As may be understood, the wearable device  108  may include multiple motion sensors  202  to sense motion of the user. In one example, the motion sensor  202  can be an inertial measurement sensor (IMU), accelerometer, gyroscope, or the like. 
     According to an aspect, the wearable device  108  may include a brainwave sensor  204  that may sense activities of the user&#39;s brain. For example, the brainwave sensor  204  may sense brain activities corresponding to the gesture made the user&#39;s body part. In addition, the brainwave sensor may sense the brain activities related to the control action for the remotely controlled appliances  102 . Accordingly, the brainwave sensor  204  may generate an electroencephalogram (EEG) signal. In an example, the brainwave sensor  204  can include EEG pads installed inside the wrist borne wearable device  108 - 1  may pick brain signals travelling through the nerves of the user. In another example, the EEG pads inside the head borne wearable device  108 - 2  may make contact with the scalp of the user and may pick brain signals from the scalp. In yet another example, the EEG pads in the neck borne wearable device  108 - 3  may be installed to be in the proximity of the nerves when worn around the neck. The brainwave sensor  204  may pick a variety of signals from the nerves of the user, including the signal that relates to a control action for the appliance. 
     The wearable device  108  may also include a processor  206  that may be operably coupled to the motion sensor  202  and the brainwave sensor  204 . The processor  206 , in operation, may process the signals received from the motion sensor  202  and the brainwave sensor  204  to select the remotely controlled appliance and determine the control action for the selected remotely controlled appliance  102 . The processor  206  can be a single processing unit or a number of units, all of which could include multiple computing units. The processor  206  may be implemented as one or more microprocessors, microcomputers, microcontrollers, digital signal processors, central processing units, state machines, logic circuitries, and/or any devices that manipulate signals, based on operational instructions. In the illustrated implementation, the processor  206  may include a signal processing module  206 - 1 . The signal processing module  206 - 1 , in operation, may process the inputs signals coming from the motion sensor  202  and the brainwave sensor  204  for further processing. In one example, the signal processing module  206 - 1  may separate the EEG signals corresponding to the gesture made by the user&#39;s body part and an EEG signal corresponding to the control action from the gamut of EEG signals received from the brainwave sensor  204 . In another example, the signal processing module  206 - 1  may also measure a strength of a wireless signal transmitted by the wireless module  104 - 2 . Further, the signal processing module  206 - 1  may uses the measured strength of the wireless signal to determine if the wearable device  108  is present in the environment  100 . The manner by which the signal processing module  206 - 1  operated will be discussed later. 
     According to an aspect, the processor  206  may include an appliance selection module  206 - 2  that may select the remotely controlled appliance  102  based on the signals processed by the signal processing module  206 - 1 . In one example, the appliance selection module  206 - 1  may process the motion signal to determine the remotely controlled appliance  102 . In addition, the appliance selection module  206 - 2  may process the EEG signal corresponding to the gesture of the user&#39;s body part to select the remotely controlled appliance. The appliance selection module  206 - 2  may further cross verify the selection made using the motion sensor and the EEG signal to finalise the selection. The processor  206  may also include control action selection module  206 - 3  that may determine the control action based on the EEG signal corresponding to the control action. The manner by which the appliance selection module  206 - 2  and the control action selection module  206 - 3  operate in conjunction with the signal processing module  206 - 1  would be explained in details later. 
     In one example, the processor  206  may further be coupled to a memory  208  which may further include computer readable instructions for the processor  206 , reference information and signals related to spatial orientation of the remotely controlled appliance  102  in the environment  100 . In addition, the memory  208  may store predefined signals related to control actions associated with all the remotely controlled appliances  102  in the environment  100 . Although the memory  208  is shown as a separate component from the processor  206 , the memory  208  can be in-built inside the processor  206 . In operation, the data stored in the memory  208  is accessed by the processor  206  for selecting the appliance and thereafter controlling the appliance based on the user&#39;s input. In another example, the processor  206  may append or update the data present in the memory  208  to recognize the gesture made by the user and the control action for the remotely controlled appliances  102 . Further, updating the data present in the memory  208  may be a part of training of the wearable device  108  to recognize and record the gesture. The manner by which the memory  208  is updated will be discussed later. 
     According to an aspect, the wearable device  108  may also include a wireless interface  210  that may communicate with the appliance over a wireless network. The wireless interface  210  can be a Wi-Fi or a Bluetooth transmitter, or both. In one example, the wireless interface  210  may perform two operations. First, the wireless interface  210  may establish a wireless network connection with the RF module  104 - 1  of the appliance. In one example, the wireless connection can be a Bluetooth link or Zigbee link or Wi-Fi link. Second, the wireless module  108  may relay the control instructions to the appliance controller  104 . 
     According to an aspect, the wireless module  108  may also sense a strength of the wireless signal from the remotely controlled appliance  102 . Strength of the signal may be used to determine the position of the wearable device  108 . Determining the position of the wearable device  108  with respect to the appliance may facilitate in determining if the wearable device  108  is present in the environment  100 . Further, presence of the wearable device  108  in the environment  100  may facilitate the wearable device  108  in the remotely controlled appliances  102  present in the environment  100  from other remotely controlled appliance present in the other environments. The recognition may further prevent in conflict in the operation of the remotely controlled appliance  102  in case any other remotely controlled appliance  102  that can be operated by the same gesture. 
     In the illustrated example, the wearable device  108  constantly monitors a strength of the wireless signal transmitted by the appliance controller  104 . Further the strength of the received signal is proportional to the distance between a transmitter and a receiver. In one example, the wearable device  108  maps the signal strength with a library of reverse signal strength indicators (RSSI) to determine if the wearable device  108  is present in the environment  100 . The RSSI may be understood as is a measure of the strength of the in a received signal. For example, the strength of the signal sensed by the wearable device  108  will be strong when the wearable device  108  is present in the environment  100 . In case, the wearable device  108  is not present in the environment  100  or the user wearing the wearable device  108  travels from the environment  100  to another environment (not shown), the strength of the signal from the appliance controller  104  in the environment  100  would get weak. Simultaneously, strength of the signals from a appliance controller present in the other environment would grow strong. Thus change in signal strength facilitates to the determine the position of the wearable device  108 . Accordingly, the wearable device  108  can be configured to operate the remotely controlled appliances present in the other environment. 
     For the following description explaining the operation of the wearable device  108 , the wearable device  108  is borne by the user on his/her wrist and the wearable device  108  is present in the environment  100 . Prior to the operation, the memory  208  is loaded with reference signal corresponding to the spatial orientation of all the remotely controlled appliances  102 . In addition, the memory  208  is loaded with a library of predefined signals corresponding to all possible control actions for all the remotely controlled appliance  102 . The memory  208  is also loaded with the RSSI. 
     In order to select the remotely controlled appliance  102 , the user may raise and point the arm toward the appliance  102  to be selected. As the user swings the arm, the motion sensor  202  senses the movement of the arm and accordingly, generates a motion signal. The motion signal is then communicated to the processor  206 . 
     Simultaneously, the brainwave sensor  204  senses the nerve signals travelling through the user&#39;s nerve corresponding to the movement of the arm and generate the EEG signals. In one example, the signal from the brain includes the signals for the arm&#39;s muscle to raise the arm. In addition, the nerve signal includes signals for individual fingers of the user&#39;s hand. In the illustrated implementation, the brainwave sensor  204  may sense different nerve signals and accordingly, generates different EEG signals. As mentioned previously, the brainwave sensor  204  may generate a first EEG signal based on the movement of the user&#39;s first body part. The brainwave sensor  204  may also generate a second signal based on a movement of a second part of the user. For example, the user may swing his/her arm that corresponds to the selection of the remotely controller appliance  102  and also moves the fingers to make a gesture that corresponds to the control action. In such a scenario, the brainwave sensor  204  generates the first EEG signal based on the movement of the arm and generates the second EEG signal based on the movement of the fingers. In addition to the first and the second EEG signal, the brainwave sensor  204  also generates additional EEG signals based on the. Once generated, all the EEG signals are relayed to the processor  206  for further analysis. 
     Once the motion signal and the EEG signals are received, the processor  206  may determine the remotely controlled appliance  102  to be selected based on the motion signal. In one example, the signal processing module  206 - 1  may receive the motion signals and EEG signals and process the signal for further analysis. For example, the signal processing module  206 - 1  may remove attenuation present in the both the signals. Once the signals are processed, the signal processing module  206 - 1  proceeds to selected the remotely controlled appliance  102 . 
     In an example, the signal processing module  206 - 1  process the EEG signals to select the remotely controlled appliance  102 . In an example, the processor  206  may distinguish the first signal and the second signal from a gamut of EEG signals received from the brainwave sensor  204 . In one example, the processor  206  may perform independent component analysis (ICA) on the EEG signals. The ICA can be understood as a statistical technique for decomposing complex dataset, which in this case, are EEG signals into individual signals. Generally, ICA may include steps, for example, centering the dataset, performing singular vector decomposition of the dataset to identify the Eigen values and other matrices. As mentioned previously, the EEG signal received by the processor  206  includes various signals including the first EEG signal and the second EEG signal. In one example, the processor  206  may separate the first EEG signal and the second EEG signal from the mixture of EEG signals. 
     Once the signal processing module  206 - 1  distinguishes between the first EEG signal and the second EEG signal, the appliance selection module  206 - 2  processes the distinguished signals separately to select the remotely controlled appliance  102 . In one example, the processor  206  may search for a reference signal stored in the memory  210  corresponding to the first EEG signal. Once a match is found, the processor  206  determines the remotely controlled appliance  102  to be controlled. In addition, the processor  206  also maps the motion signal with a reference signal that corresponds to the spatial orientation of the remotely controlled appliance  102 . For example, the motion signal is mapped to the reference signal which corresponds to the x, y coordinates of the position of the remotely controlled appliance  102 . Thereafter, the appliance selection module  206 - 2  verifies if the remotely controlled appliance  102  determined based on the first EEG signal is the same as the remotely controlled appliance  102  determined based on the motion signal. 
     Once verified, the control action selection module  206 - 3  determines the control action for the selected remotely controlled appliance  102 . In the illustrated implementation, the control action selection module  206 - 3  search through the database in the memory  208  to find a predefined signal that corresponds to the second EEG signal. For example, the second EEG signal may relate to a pinch gesture made by the user&#39;s finger. Accordingly, the control action selection module  206 - 3  would search for the predefined signal corresponding to the pinch gesture made for the selected remotely controlled appliance  102 . Once a match is found, the second EEG signal is mapped to the predefined signal. Based on the mapping, the control action selection module  206 - 3  determines the control action for the selected remotely controller appliance  102 . In one example, the control action associated with the selected remotely controlled appliance  102  is to switch ON the selected remotely controlled appliance  102 . 
     Once the selection of the remotely controlled appliance  102  and determination of the control action is complete, the processor  206  communicates relevant information to the wireless interface  210 . In an example, the relevant information includes the instructions to establish communication with the selected remotely controlled appliance  102  and the control action to be performed on the selected remotely controlled appliance  102 . Once the relevant information is received by the wireless interface  210 , the wireless interface  210  establishes a wireless connection with the selected remotely controlled appliance  102 . In an example, the wireless interface  210  may communicate with the RF module  104 - 1  (shown in  FIG. 1 ) of the appliance controller  102  to establish a wireless connection. As mentioned previously, the communication can be established based on any wireless protocol known in the art. Once the communication is established, the wireless interface  210  relays the control actions to the RF module  104 - 1 . Upon receiving the control actions, the RF module  104 - 1  communicates the control action to the microcontroller  104 - 1 . The microcontroller  104 - 1  then operates the remotely controlled appliance based on the control action. 
     In an additional aspect of the present subject matter, the wearable device  108  may also determine the position of the wearable device  108  with respect to the remotely controlled appliance  102  in the environment  100 . There may be a case where the user may move between different environment  100 . In such scenario, the wearable device  108  can determine the position with respect to the environment  100 . In one example, the wearable device  108  may measure a strength of the signal transmitted by the appliance controller  104 . As may be understood, the strength of the signal received by the wearable device  108  is sensed by the wireless interface  210  and the strength is communicated to the processor  208 . As the wearable device  108  moves away from the environment  100 , the strength sensed by the wearable device  108  weakens. Simultaneously, the processor  208  constantly map the sensed strength against library of RSSI stored inside the wireless interface  210 . Once the processor  208  finds a match of the reference signal with an RSSI indicative of the exit of the wearable device  108  from the environment  100 , the processor  206  determines that the wearable device  108  has moved out of the environment  100 . 
     Simultaneously, the processor  208  may also map a strength of a signal coming from the second appliance controller of the second environment and checks if the strength of the signal is growing strong as the wearable device  108  moves between the environments. Hereto, the wearable device  108  maps the received signal with the RSSI to check if the wearable device  108  has entered into the second environment  100 . Once the processor  208  finds a match of the reference signal with an RSSI indicative of the entry of the wearable device  108  from the environment  100 , the processor  206  determines that the wearable device  108  has moved into the second environment  100 . 
     According to one implementation of the present subject matter, the wearable device  108  can also be trained to record and recognize gestures related to the aforementioned operation. In one example, the wearable device  108  store information regarding the gesture performed by the user during training of the wearable device  108  in the memory  208 . During the training operation, the user performs the gesture which is to be recorded. As the user performs the operation, the brainwave sensor  204  senses the brain activity and accordingly, generates the EEG signals. The EEG signals are then communicated to the processor  206 . Thereafter, the processor  206  performs the ICA to distinguish the first EEG signal and the second EEG signal from the rest of the EEG signals. Once distinguished, the processor  206  records the first EEG signal and the second EEG signals in the memory  208  as a reference signal and a predefined signal respectively for the gesture performed by the user. In one example, the processor  206  records the first EEG signal and the second EEG signal as a time varying function of the electrical signal stored in the memory  208 . 
     Once recorded, same operation is conducted to obtain a concurrent reading. For example, the recording operation is performed 5-6 times in order to obtain a concurrent reading. Once a concurrent reading is obtained, the concurrent reading is stored as the reference signal for the gesture. 
       FIG. 3  illustrates a method  300  for contactless interaction with a plurality of remotely controlled appliances  102  present in an environment  100  by a wearable device  108  worn by a user. Any number of the described method blocks can be combined in any order to implement the method  300 , or any alternative methods. Additionally, individual blocks may be deleted from the method  300  without departing from the scope of the subject matter described herein. The method  300  is described with reference to the wearable device  108  of  FIGS. 1, 2   a , and  2   b.    
     At block  302 , a motion signal corresponding to the user&#39;s first body part is generated by the motion sensor  202 . For example, the motion sensor  208  senses when the user swings the arm and/or points the finger towards the remotely controlled appliance  102 . Accordingly, the motion sensor generates the motion signal. The motion signal is then communicated to the processor  206 . 
     At block  304 , EEG signals related to the brain activity of the user is measured by the brainwave sensor  204 . As mentioned previously, the brainwave sensor  204  may generate a first EEG signal based on the movement of the user&#39;s first body part. The brainwave sensor  204  may also generate a second signal distinct based on a movement of a second body part of the user. Accordingly, the brainwave sensor  204  generates the EEG signals and communicates the EEG signals to the processor  206 . 
     At block  306 , the remotely controlled appliance to be controlled, is selected. The selection is performed by using the motion signal and the EEG signal. In one example, the processor  206  may map the motion signal with a reference signal that corresponds to the spatial orientation of the remotely controlled appliance  102 . For example, the motion signal is mapped to the reference signal which corresponds to the spatial orientation of the remotely controlled appliance  102  in the environment  100 . 
     In addition, the processor  206  processes the EEG signals to select the remotely controlled appliance  102 . In an example, the processor  206  may distinguish the first signal and the second signal from a gamut of EEG signals received from the brainwave sensor  204 . As described previously with respect to  FIGS. 2 a  and 2 b   , the processor  206  may separate the first EEG signal and the second EEG signal. Once distinguished, the processor  206  may search for a reference signal in the memory  210  corresponding to the first EEG signal. Once a match is found, the processor  206  determines the remotely controlled appliance  102  to be controlled. Thereafter, the processor  206  check if the remotely controlled appliance  102  determined based on the first EEG signal is the same as the remotely controlled appliance  102  determined based on the motion signal. 
     At block  308 , a control action for the selected remotely controlled appliance  102  is determined. In one example, the second EEG signal is mapped against a predefined signal which corresponds to the motion of the user&#39;s second body part. 
     At block  310 , the selected remotely controlled appliance  102  is operated based on the determined controlled action. In one example, the processor  206  communicates information to the wireless interface  210  and accordingly, the wireless interface  210  establishes a wireless connection with the RF module  104 - 1  to communicate the control action. Once the control action is communicated, the appliance controller  104  operates the remotely controlled appliance  102 . 
       FIG. 4  illustrates a network environment  400  using a non-transitory computer readable medium  402  for contactless operation of the remotely controlled appliances  102  (shown in  FIG. 1 ), in accordance with one implementation of the present subject matter. In one example, the network environment  400  includes a processing resource  404  communicatively coupled to the non-transitory computer readable medium  402  through a communication link  406 . 
     For example, the non-transitory computer readable medium  402  can be, for example, an internal memory device or an external memory device. In one example, the communication link  406  may be a direct communication link, such as one formed through a memory read/write interface. In another example, the communication link  406  may be an indirect communication link, such as one formed through a network interface. In such a case, the processing resource  404  can access the non-transitory computer readable medium  402  through a network  408 . The network  408  may be a single network or a combination of multiple networks and may use a variety of communication protocols. 
     The processing resource  404  and the non-transitory computer readable medium  402  may also be communicatively coupled to data source  410 . The data source  410  can include, for example, databases and computing devices. The data source  410  may be used by the database administrators and other users to communicate with the processing resource  404 . 
     In one example, the non-transitory computer readable medium  402  includes a set of computer readable instructions, such as appliance selection module  412  and a control action selection module  414 . The set of computer readable instructions, referred to as instructions hereinafter, can be accessed by the processing resource  404  through the communication link  406  and subsequently executed to perform acts for contactless operation. 
     For discussion purposes, the execution of the instructions by the processing resource  404  is similar to the processor  206  that has been described with reference to various components introduced earlier with reference to description of  FIGS. 1, 2   a  and  2   b.    
     On execution by the processing resource  404 , the appliance selection module  412  maps the motion signal and the EEG signal against the reference signal stored in the memory  208  to determine the remotely controlled appliance  102 . In one example, the appliance selection module  412  maps the received motion signal against the reference signal that corresponds to the spatial orientation of the remotely controlled appliance  102 . 
     In addition, the control action selection module  414  analyzes the EEG signal to determine the control action for the selected remotely controlled appliance  102 . In one example, the control action selection module  414  maps the EEG signal against predefined signals stored in the data source  410  to determine the control action corresponding to the EEG signal. 
     Once the remotely controlled appliance  102  is selected and the control action is determined, the communication module  416  communicates the relevant information to the wireless interface  210  to operate the selected remotely controlled appliance  102 . The communication module  416  can also determine the position of the remotely controlled appliance  102 . The communication module  416  maps a strength of the signal of the RF module  104 , received by the wireless interface  210 , against the RSSI to determine if the wearable device  108  is present the environment  100  (shown in  FIG. 1 ). 
     Although the present subject matter has been described with reference to specific embodiments, this description is not meant to be construed in a limiting sense. Various modifications of the disclosed embodiments, as well as alternate embodiments of the subject matter, will become apparent to persons skilled in the art upon reference to the description of the subject matter.